Scaling Artificial Intelligence
in Health
Scaling Artifi
cial Intelligence in H
ealth
Scaling Artificial Intelligence
in Health
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Foreword
Artificial intelligence (AI) has the potential to transform how healthcare operates, is delivered, and is
experienced by patients. Since 2019, the OECD has shown leadership in this area through the publication
of its AI Principles and the creation of supporting initiatives such as the Policy Observatory,
expert groups, and publications promoting the responsible use of AI. In 2024, OECD Health Ministers
endorsed the Declaration on Building Better Policies for More Resilient Health Systems, which
acknowledged, among other priorities, “the importance of adopting a sector-specific approach to
developing appropriate policies around the use of artificial intelligence in health, while taking into account
multi-sectoral contexts, such that the benefits in areas such as health system resilience can be realised
fully.” This report – and its policy checklist – aims to support that objective.
This report goes on to review OECD Members’ progress in taking action to advance the responsible scale
of AI in their health systems. It is clear that while progress is being made, there is still significant work to
be done in areas of trust and capacity building; strengthening data quality, access, protection, and use;
and leadership to guide and oversee action in the implementation of AI in Health.
This report was prepared by Eric Sutherland, Rachel Fellner and Yunona L’Heureux at the OECD Health
Division within the Directorate for Employment, Labour and Social Affairs (ELS). This report is part of the
OECD Horizontal Project on Thriving with AI: Empowering Economies and Societies. The authors would
like to thank colleagues in OECD’s Science. Technology, and Innovation (STI) and Governance (GOV)
directorates for their extensive comments, input and direction. A number of colleagues provided meaningful
comments and direction, including Lucia Russo and Limor Schmerling-Magazanik (with STI), and Ricardo
Zapata and Jamie Berryhill (with GOV). The authors would also like to thank colleagues from ELS including
Francesca Colombo and Mark Pearson.
The authors are grateful to Bogi Eliasen (Movement Health), David Novillo and Clayton Hamilton (World
Health Organization – Europe), and Simon Hagens and Ronan O’Kelly (Global Digital Health Partnership
– GDHP) for their comments and suggestions in preparation of the earlier document drafted for Health at
a Glance 2023 and used in the GDHP/OECD Policy Repository Tool.
The authors are also appreciative of the efforts of the OECD AI in Health Expert Group who were
instrumental to the development of this checklist and the drafting process. Members of the of the expert
group are included at the end of this document (see Annex A). The authors would also like to thank the
GDHP, and Coalition for Health AI (CHAI) who offered helpful advice on the comprehensiveness and
priority of the items in this checklist. The authors would also like to thank Sasa Jenko (European
Commission), and Kathrin Cresswell and Robin Williams (University of Edinburgh) for their comments and
suggestions to the document.
Finally, the authors would like to thank the Health Foundation for financial support in the development of
this checklist and report.
The views expressed in this document are the views of the authors and not necessarily the views of any
OECD country or individual expert.
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Table of contents
Foreword 3
Executive summary 6
1 Responsible scale of AI in health 9
Rationale for formalising an approach at the intersection of AI and health 10
References 12
Notes 13
2 AI in Health Policy Checklist 14
Advancing an AI in Health Policy Checklist 14
Four thematic pillars of an AI in Health Policy Checklist 15
AI in Health Policy Checklist 16
Using the AI in Health Policy Checklist 26
References 28
3 Current state of implementation of AI in health across OECD Members 32
Review of progress toward the responsible scale of AI in health 32
Priorities for the responsible scale of AI in health 48
References 50
4 Scaling fast while doing no harm 58
Annex A. Members of the OECD AI in Health Experts Group 60
FIGURES
Figure . Policy checklist to support responsible scale and scale of AI in health 15
TABLES
Table . Policy checklist to support responsible scale and scale of AI in health 27
Table . Components of national AI in health strategy/action plans 34
Table . Guardrails to establish agreed objectives for AI in health 35
Table . Leading practices to enable better use of health data 38
Table . Practices enabling the use of AI 41
Table . Enabling capacity and capability of health workforce 44
Table . Components of national AI capacity building in health across OECD countries 45
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Table . State of AI in health oversight 46
Table . State of public engagement for AI in health 47
Table . Readiness for the responsible scale of AI in health by policy action 49
Table . Readiness for the responsible scale of AI in health by select policy areas 58
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Executive summary
Artificial Intelligence (AI) holds significant potential for the healthcare system. That potential is not being
fully realised due to fragmented data foundations, non-aligned policies and practices, and structural
and governance barriers to scalability. Although AI is universally used in administration across OECD
Member countries (100%), national-level scale-up remains limited, (. only 10% for medical imaging
applications).
Today, there are well-documented risks associated with the use of AI in healthcare, such as skewed
data, privacy and security risks, insufficient transparency or oversight, and the potential for job
displacement and de-personalisation. While caution is necessary, there is also risk in inaction.
The opportunity from AI in health will be unleashed when we can responsibly scale. This requires a
balance between market forces (that move fast), health culture (doing no harm), and reaching every person
(through scale).
While AI is already being used in health across OECD Member countries, responsible and scalable
adoption remains impacted by structural, regulatory, and governance gaps. OECD Member countries are
undertaking initiatives to address these gaps:
• Establishing a strategy or action plan at the intersection of AI and health (18%),
• Establishing an oversight body for the use of AI in health (18%),
• Establishing a national approach to regulatory sandbox with a focus on AI in health (18%),
• Streamlining the national approach to health technology assessments to include AI (24%),
• Updating national procurement guidelines to account for AI in health (11%),
• Establishing national approach to improve the use of AI in the health workforce (29%), and
• Developing national legislation for AI in health (3%).
To help support these actions toward the responsible scale of AI in health, a coherent policy checklist was
developed to guide decision making and prioritisation. The checklist is built on OECD AI principles and
frameworks and developed in partnership with the Global Digital Health Partnership (GDHP) and Coalition
for Health AI (CHAI) as well as the OECD AI in Health Expert Group.
This AI in Health Policy Checklist identifies policymaker, technologist, and health workforce actions to
responsibly scale AI in health. Critically, the checklist can be used to identify blind spots in those
actions. The checklist is not prescriptive; however, it provides a prompt for decision makers to consider a
full range of action across relevant policy categories and areas.
The four pillars of the checklist focus on establishing enablers (for data foundations, assuring and
scaling AI, and capacity building); implementing guardrails (to oversee and monitor progress towards
agreed objectives); engaging meaningfully with the public, providers, and industry; and deploying
trustworthy AI. Across the four pillars, nine main policy categories and 42 questions have emerged as
critical for responsibly scaling the benefits of AI in health:
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Establishing Enablers
• Better use of data – Without data, AI solutions cannot function effectively. Considerations for data
in healthcare include that they are findable, accessible, interoperable, and reusable (FAIR), along
with being representative of the population for both primary and analytic uses. Emerging leading
practice includes the establishment of country-led health data authorities (. across Europe) of
equivalent governance structures to ensure compliance with data protection laws while also
facilitating AI adoption using secure and quality datasets.
• Guidance to enable scale of AI – To support industry (developers) and implementers
(governments, health workforce, public) to move AI from pilot to widespread deployment, tailored
policy guidance is needed. An emerging leading practice includes the development of model cards
(. from the Coalition for Health AI), which certify compliance, transparency, and accountability
of AI solution when applied to real-worlds settings.
Capacity and capability
• People capacity – A skilled and knowledgeable health workforce is essential for the uptake and
sustained use of AI solutions in healthcare. Emerging leading practices include proactive planning
and workforce upskilling across both frontline and back-end health roles (. the United Kingdom
Digital and Data Professional Capability Framework).
• Technical capacity – A secure, interoperable and adaptive technical infrastructure
(. computing capacity, data storage, connectivity) is a cornerstone for deploying and scaling AI
solutions from local to cross-national levels. Robust infrastructure ensures that AI tools can process
large, complex datasets in real time, integrate across diverse health information systems, and
operate safely and reliably to support both primary and analytic use of health data.
Implementing Guardrails
• Agreeing upon common objectives – Support the development of common guardrails,
strategies, and collective activities, which ensure the streamlined development and implementation
of AI in health. An emerging leading practice includes the development of a national strategy that
addresses the unique aspects of AI development, deployment and use in healthcare. Such
strategies have been developed by seven OECD countries with several under development.
• Oversight, measurement and monitoring – Given the rapid advancements of AI technologies, it
is necessary to understand the potential benefits and risks while taking action to optimise benefit
while protecting from harms. Emerging leading practices include the development of indicators to
assess clinical effectiveness and economic impact of AI at scale.
Engaging Meaningfully
• Public – It is critical to engage and educate the public to foster trust in AI in health and increasingly
empower their active participation. An emerging leading practice includes the establishment of
public assemblies to integrate public voice into the work on AI in Health (. The French citizen’s
assembly for digital health).
• Healthcare providers – Many AI solutions in health are used by both front and back-end
healthcare workers. An emerging leading practice includes mandating education on AI within the
curriculum of health professionals (. in Korea and others).
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• Industry – Active engagement and collaboration with industry to support literacy, common
understanding, and alignment in AI solutions. Emerging leading practices include developing
collaborative and transparent processes for industry to engage with governments to test, validate,
and support the integration of AI solutions (. the United Kingdom NHS AI Lab).
Trustworthy
• Trustworthy use of AI – Trust underpins the use of AI in health. There is an imperative to ensure
that humans, and health promotion are at the core of any AI solution brought into the health
ecosystem, while doing no harm. Emerging leading practices include the development and use of
ethical impact assessments for AI solutions in health (. New Zealand’s checklist embedding
ethics in tool evaluation).
While many countries are making individual progress, there is strategic opportunity for multi-lateral
collaboration to reduce the unnecessary barriers to scale. A shared recognition is emerging: coherent,
cross-border compatible policies are essential to balance innovation with safety, and economic
opportunity with building public trust.
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Interest in artificial intelligence (AI),1 is exponentially growing in healthcare. While the main developments
to date have been predominantly made in medical imaging and automating administrative tasks,
advancements in other fields will likely follow and become an integral part of healthcare (OECD,
forthcoming workforce publication, 2026), (Secinaro et al., 2021[1]). Today, AI is used in health systems
across all OECD countries (100% of those interviewed. However national-level scale-up remains
limited; only 10% of medical imaging applications being used on a national scale (OECD,
forthcoming workforce publication, 2026).
Beyond imaging, AI is enhancing analytical capabilities, diagnostic accuracy, patient monitoring, health
system administration, drug discovery, and workflow efficiency. Other areas, such as predictive medicine,
personalised medicine and health literacy can also meaningfully benefit from advances in AI, resulting in
more human-centred care.
At the same time, the adoption of AI adds risks due to potential workforce displacement, concerns over
data protection and security, the application of AI trained on skewed data, and unequal access to the
benefits from AI solutions.
Many of these risks arise from the challenge of scalability of AI solutions across institutions, regions,
countries and populations. Consider the time it took to reach 40% consumer adoption of previous
technologies across sectors: 64 years for the telephone, 45 years for electricity, 23 years for computers,
16 years for mobile phones, and 13 years for the internet (DeGutsa, 2012[2]). The question remains, “How
long will it take AI to scale, when considering the societal acceptance and ethical debate for AI in
healthcare?”. In some cases, the barriers to AI adoption are justified, given the concerns about the current
state of safety of these systems in the health sector. In other cases, barriers to scaling arise from the lack
of robust policy; fragmented data and digital infrastructure; minimal co-ordination across the AI in health
ecosystem; governance frameworks that fail to incentivise safe and scalable solutions; and previous efforts
that eroded trust among both patients and healthcare professionals.
The difficulty to scale AI solutions in health undermines its potential economic and health benefits. The
inability to scale leads to duplicative investments and poor-quality solutions based on data that are not
representative of target populations. Without action, the benefits of AI will not be distributed evenly, and
the reach of innovation will be limited. This disparity is already evident, with wealthier institutions able to
invest in the necessary technical infrastructure, financial resources and skilled personnel to implement and
sustain AI solutions whereas less affluent institutions lack the people, data, or infrastructure to design,
develop, implement, sustain, and evolve these AI solutions. A fragmented approach leads to AI solutions
that are more expensive and generate sub-optimal results with limited reach.
A collective approach to AI in health – where governments partner with stakeholders to act on their role as
stewards of the health system – is necessary to foster safety, privacy, and innovation. This approach can
enable governments to proactively shape the integration of AI into health systems while ensuring alignment
with public interest objectives.
To that end, in January 2024, OECD Health Ministers, in the Declaration on Building Better Policies for
More Resilient Health Systems (
outlined several key objectives related to the use of AI in the health sector:
1 Responsible scale of AI in health
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“assist in our efforts to harness the transformative potential of new technologies for health such as genomics
and genetics, and artificial intelligence, while ensuring that their application delivers better health outcomes
by tracking and assessing the health and health systems implications including on health spending of these
developments”,
“assess and make policy recommendations on the implications of using artificial intelligence in health
systems”,
“develop a sector-specific framework for AI in health, that is aligned with existing multi-sectorial frameworks,
to encourage responsible use of artificial intelligence, exploiting cross-sectoral synergies to promote fairness,
transparency and accountability, while ensuring consistency across policy domains”.
Based on the Declaration, the OECD established a time-bound AI in Health Expert Group within the
Network of Experts in May 2024 (OECD, 2024[3]). This Expert Group involves individuals from
27 countries, representatives from leading organisations (. World Health Organization, Coalition for
Health AI, HealthAI), and members of civil society. Aligned with the 2024 Health Ministerial Declaration,
and building on the OECD Recommendation for Health Data Governance (OECD, 2016[4]) and OECD AI
Principles (OECD, 2024[5]), the Expert Group was focussed on addressing the challenge of scalability for
AI in Health. To that end, the experts focussed their efforts on developing an AI in Health Policy Checklist
for the responsible scale of AI in health, considering both within jurisdictions and across borders. In total,
the group has met six times to develop the checklist.
This report is structured around three core components: the rationale for a sector-specific approach; the
“AI in Health Checklist”; and its application to assess readiness for the responsible scale of AI in health:
1. Rationale for the responsible scale of AI in health (Why): The case for leadership and policy
clarity at the intersection of AI and health, outlining why a formalised approach is beneficial to guide
safe and responsible scale (remainder of Chapter 1).
2. AI in Health Policy Checklist (What): A tailored AI in Health Policy Checklist, developed through
expert consultations and review of existing evidence and structured around the pillars of Enablers,
Guardrails, Engagement, and Trustworthiness. (Chapter 2).
3. Current state of implementation of AI in health across OECD Members (How): OECD
countries are advancing the implementation of AI in health systems across legislation, governance,
technical infrastructure and capacity. Leveraging the AI in Health Policy Checklist, an analysis of
the current state of implementation is structured around core components of checklist (Chapter 3).
The report closes by summarising the report and identifying possible future actions for policy work at the
intersection of health and AI (Chapter 4).
Rationale for formalising an approach at the intersection of AI and health
The transformative potential of AI in health is clear, but so are the risks. Without a coherent comprehensive
AI in Health Policy Checklist, countries face fragmented adoption, uneven guardrails, and unclear return
on investment. This may lead to challenges of trust or unsustainable return on investment. This is
exacerbated in countries with a federated governance structure – such as Germany, Australia or Canada
– where the accountability for health is at the state or provincial level can cause further fragmentation. A
checklist could help regional health authorities align their activities for collective benefit while enabling local
delivery of health services.
To further reinforce the case for an AI in Health Policy Checklist, key concerns related to privacy, safety,
validation and value have been identified. These interconnected areas exhibit challenges that are
significant considerations in the health sector due to concerns about the potential for human harm and the
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opportunity for human benefit. Without a focus on policies that address these concerns, there is risk that
decisions are delayed or inconsistent. The OECD AI in Health Policy Checklist aims to help policymakers
identify key challenge areas and provide insights into emerging practices for actionable steps. The
following sections provide context on these concerns and the benefit of a collaborative approach.
Health data access, use, privacy, and security
The OECD Recommendation on Health Data Governance (OECD, 2016[4]) provides guidance in several
areas related to health data access, use, privacy, and security. The Recommendation provides
considerations for protecting privacy and security of individual health data, while also supporting the use
of data for purposes of direct patient care, health system management, public health, and innovation (the
latter three of which are considered examples of analytic data use).
This will be especially important for amplifying the benefits and addressing the risks of emerging
technologies and capabilities such as quantum computing (OECD, 2025[6]) and genomics (OECD, 2021[7]).
Consistency in this practice fosters trust of the public and providers while enabling an efficient foundation
on which AI solutions are built and being prepared to adapt to future opportunities and risks. A checklist
will help avoid blind spots in establishing that consistency.
Health safety, quality and equity
If health technologies fail, they can have long-lasting and profound consequences on trust among health
providers and the public. Likewise, failure to leverage the advanced capabilities of AI in the provision of
health can also cause harms from inaction. As opportunities to improve healthcare quality emerge – due
to the use of technology including AI – the standards of healthcare quality and safety will evolve.
If solutions are not designed to scale, reaching the quality-of-care standards will become more difficult for
low-resource or hard-to-reach settings to achieve. This includes supporting health in remote and rural
areas, supporting those with poor health and data literacy, and assuring indigenous data sovereignty
(where applicable).
Enabling responsible scale of solutions will help the benefits of AI reach every person. A checklist will help
identify blind spots for barriers to scale the benefits of AI while assuring safety.
Validating innovation for use in health
Emerging studies indicate that 75% of AI in health solutions evaluated through randomised control trials
demonstrate a positive impact (Han et al., 2024[8]). For emerging innovators, particularly small and medium
enterprises (SMEs), there is a risk that the fragmented patchwork of policies and related processes slow
down innovation and competitiveness.
While variation in policies and processes is expected across jurisdictions and countries, unnecessary
variation can add significant costs (. financial, time) for researchers and innovators without
commensurate value of increased protection for the jurisdiction or the public. The unintended cost of
variation includes lack of clarity for how approvals will operate. For emerging capabilities, such as adaptive
and agentic AI2 systems 3, the health system has been slow to clarify their approach across medical device
approvals and health technology assessments among other areas.
Enabling a consistent approach will support capacity and capability building toward a robust innovation
ecosystem across delivery, approvals, and scale. A checklist will help to identify blind spots in establishing
that ecosystem that accelerates innovation for all.
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Value dynamics of healthcare
The evaluation of AI initiatives in health mirror those in the existing landscape of health system valuation,
which considers a wide range of indicators – generally aligning with the quintuple aim for healthcare
improvement (Nundy, Cooper and Mate, 2022[9]). Benefits from AI in health could translate to better health
outcomes (. using AI in medical imaging to improve accuracy diagnosis and treatment), healthcare
experience (. optimising and reducing waiting time), provider experience (. reduced administration
leading to more time to patient care), improved equitability (. identifying populations that would benefit
form a targeted health intervention), and reduced cost (. reducing redundant testing, optimisation of
sparse resources, improvements to productivity). For example, evidence from the American Medical
Association (AMA) shows that Ambient Voice Technology reduced documentation time by %, saving
an average of minutes per case compared to manual typing (Karavassilis et al., 2025[10]).
Importantly, these benefits may be achieved over both a long-time horizon and broad population. Within
the private sector and pharmaceutical research and development, AI also offers promise to improve the
discovery, development and supply chain delivery of new therapies (Sanofi, 2023[11]).
The benefits of AI in health can be well understood, but analysis is needed to evaluate the social and
economic impact of AI solutions. A checklist will help avoid blind spots that prevent understanding and
evaluating those benefits across populations and geographies.
References
DeGutsa, M. (2012), Are Smart Phones Spreading Faster than Any Technology in Human
History? | MIT Technology Review,
than-any-technology-in-human-history/,
than-any-technology-in-human-history/ (accessed on 15 October 2025).
[2]
Han, R. et al. (2024), “Randomised controlled trials evaluating artificial intelligence in clinical
practice: a scoping review”, The Lancet Digital Health, Vol. 6/5, pp. e367-e373,
[8]
Karavassilis, M. et al. (2025), “Ambient Voice Technology in Same Day Emergency Care:
Enhancing Documentation Efficiency and Patient Flow”, Acute Medicine, Vol. 24/1, pp. 49-57,
[10]
Nundy, S., L. Cooper and K. Mate (2022), “The Quintuple Aim for Health Care Improvement”,
JAMA, Vol. 327/6, p. 521,
[9]
OECD (2026), “The agentic AI landscape and its conceptual foundations”, OECD Artificial
Intelligence Papers, No. 56, OECD Publishing, Paris,
[13]
OECD (2025), “A quantum technologies policy primer”, OECD Digital Economy Papers, No. 371,
OECD Publishing, Paris,
[6]
OECD (2025), Recommendation of the Council on Artificial Intelligence,
[12]
OECD (2024), AI principles, [5]
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OECD (2024), OECD AI, [3]
OECD (2021), “Building and sustaining collaborative platforms in genomics and biobanks for
health innovation”, OECD Science, Technology and Industry Policy Papers, No. 102, OECD
Publishing, Paris,
[7]
OECD (2016), OECD Recommendation on Health Data Governance,
[4]
Sanofi (2023), Artificial Intelligence in R&D,
artificial-intelligence/artificial-intelligence-rd (accessed on 14 October 2025).
[11]
Secinaro, S. et al. (2021), “The role of artificial intelligence in healthcare: a structured literature
review.”, BMC medical informatics and decision making, Vol. 21/1, p. 125,
[1]
Notes
1 Defined as “a machine-based system that, for explicit or implicit objectives, infers, from the input it
receives, how to generate outputs such as predictions, content, recommendations, or decisions that can
influence physical or virtual environments. Different AI systems vary in their levels of autonomy and
adaptiveness after deployment” (OECD, 2025[12]).
2 Agentic AI are systems of co-ordinated agents that can break down tasks, work together over extended
periods, and use external tools to achieve complex goals. These systems are designed to operate in more
open-ended, less predictable environments and often require less human supervision than individual AI
agents (OECD, 2026[13]).
3 Artificial intelligence system is a machine-based system that, for explicit or implicit objectives, infers, from
the input it receives, how to generate outputs such as predictions, content, recommendations or decisions
that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy
and adaptiveness after deployment (OECD, 2025[12]).
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Adopting AI solutions in healthcare needs pace, focus, and buy-in to ensure that health systems can scale
fast while doing no harm. This approach must create space for vision, alignment, multi-stakeholder
collaboration and collective impact. Health is a fundamental pillar of society, yet in this domain, harm can
be irreversible – even fatal – raising the stakes for responsible adoption of AI.
Much of the challenge in health stems from needing to balance several forces at the same time – advancing
science and innovation, doing no harm, and providing equitable outcomes for all. As discussed in
Chapter 1, the use of AI in Health simultaneously comes with significant opportunities and significant risks.
Many health systems – and individual institutions – have tried to find this balance point in isolated siloes.
While delivering against local objectives, this has the potential to harm advancement for the health system
as a whole – delivering sub-optimal outcomes for individuals and populations.
Other industries have learned that collaboration on data infrastructure and digital foundations not only
reduces operational costs – it also enables targeted investments in areas that offer long-term strategic
value and improve customer experience. For example, co-operation in the financial sector enables
individuals to withdraw cash from bank machines and move funds around the world. Similarly, co-operation
among airlines simplifies the ability for consumers to book flights themselves and for airplanes to fly safely
across the globe. These industries have collaborated to align on data and technical foundations while
competing on design and implementation of advanced practices. This led to improved services, lower
overall cost, and innovation that thrives.
In health, the lack of alignment on policy, data, and technical foundations prevents health institutions from
collaborating easily. Fragmentation in foundations acts as a barrier to innovation and inhibits the ability of
health systems to move toward person-centric health systems that improve outcomes. A lack of effective
AI governance and cross-border compatibility in policy inhibits the potential of AI to scale in the digitalised
health ecosystem. This increases costs and degrades outcomes. This checklist seeks to help policymakers
by identifying potential blind spots so policy action can support AI solutions that could scale efficiently and
enable better health outcomes for all.
Advancing an AI in Health Policy Checklist
As AI becomes increasingly embedded in health systems, there is growing recognition of the need for a
co-operative approach to guide responsible scale of AI in health. While general AI principles offer valuable
high-level direction, the complexity of health systems – which seek to meet the quintuple aim for healthcare
while respecting the sensitivity of health data – demand guidance rooted in real world implementation
needs (Nundy, Cooper and Mate, 2022[1]). The AI in Health Policy Checklist provides guidance on a
co-operative approach.
The purpose of the AI in Health Policy Checklist is to identify the foundational policy areas that support the
responsible scale of AI and to understand countries’ readiness in their adoption of AI. This can help
policymakers deliver a consistent and sustainable approach. Countries can also work together to identify
how to address the compatibility of policy areas across borders. That compatibility will accelerate the scale
of innovation to optimise both human and economic benefits. This checklist does not distinguish between
2 AI in Health Policy Checklist
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specific AI modalities. Rather it provides guidance for ensuring that AI integration into health systems is
ethically grounded, operationally efficient and clinically effective. Future work for this checklist could
examine challenges with specific modalities (such as Agentic AI) or applications (such as in public health).
The checklist is anchored in cross-border collaboration and practical relevance. The process of
development drew on desk research, surveys, international partnerships and iterative consultation to
identify key policy areas. Key policy areas were identified to support the responsible scale of AI in health
across core policy areas. To translate these policy areas into actionable elements, the process drew on
the OECD’s Integrated Digital Health Ecosystem Policy Checklist (OECD, 2023[2]) and the OECD’s
Framework for Trustworthy AI in Government (OECD, 2024[3]). These tools provided a foundation for
categorising key concepts under the thematic pillars of enablers, guardrails, engagement, and
trustworthiness (See Figure ).
Figure . Policy checklist to support responsible scale and scale of AI in health
Four thematic pillars of an AI in Health Policy Checklist
An AI in Health Policy Checklist emphasises the following areas, building on OECD Governing with Artificial
Intelligence (OECD, 2024[3]).
Enablers for AI in health develop strong data and digital foundations to support timely and interoperable
health data across the health sector and government services. Data are then accessible for use in the
development, testing, implementation, and evolution of AI solutions while ensuring continuous safety of
those solutions. Processes are streamlined to review, validate, approve, deploy, and evolve AI in health
solutions, with sufficient and sustainable human and technical capacity.
Guardrails support the safe and responsible deployment of AI solutions. The guardrails utilise agreed
upon common objectives for an AI in Health programme across all stakeholders and allow all parts of the
health system to work together toward agreed goals. The guardrails also establish governance structures
that put incentives in place to deliver those objectives. Procedures are put in place to measure, monitor,
and address issues with the effectiveness of the AI in Health program. Ultimately, this establishes clear
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responsibilities across the AI lifecycle, including who is accountable for doing what with which controls,
and establishing the implications for issues as they arise in the interest of enabling efficient and effective
value of AI while protecting from harm.
Engagement for AI in health relates to the involvement of key stakeholders in the responsible scale of AI.
Key stakeholders include the public, healthcare providers, and other enabling partners. Engagement
involves building the capacity and capability to engage with the AI in Health program. A key outcome of
engagement is enabling trustworthiness to accelerate the adoption of approved AI solutions.
In addition, consideration of Trustworthiness for AI in health is necessary for consideration of bioethics
and responsible scale. A fulsome approach to trustworthiness and ethics will consider action from multiple
perspectives – the potential harm from both the implementation and the delay in implementation on AI in
Health solutions. Embedding ethics would reflect the OECD’s AI principles (OECD, 2024[4]) and the
broader perspectives of bioethics reflecting patient beneficence, patient non-maleficence, patient
autonomy, and justice.
With this as background, the following section describes each of the parts of the OECD AI in Health Policy
Checklist. This is based on the nine areas noted in Figure . With the description of the policy areas
there is a brief discussion of why it is important; reflection on emergent, or divergent practices; and notable
examples of the policy areas in practice. A table with questions for policymakers to understand their
readiness for the responsible scale of AI is included at the end of this chapter. Chapter 3 builds on the
policy checklist by examining progress across OECD countries for the responsible scale of AI.
AI in Health Policy Checklist
Enabler: Use and protection of health data in AI solutions
Why this is important: Data underpins all stages in the design, development, implementation, use,
evaluation, and evolution of AI solutions. Health data represents the largest segment of data from any
industry (estimated at 30%) and is growing faster than any other sector (Weber, 2024[5]). Yet, the health
sector is using less than 5% of the data for decision making purposes (World Bank Group, 2023[6]). Health
data has the unique property of simultaneously providing value for individual care (such as through direct
care) and for communities and the population (such as through public health measures, research,
innovation, or health system monitoring). It is important for enablers of health data to account for the risks
associated with both data use (. privacy breaches) and data non-use (. preventing safety issues,
supporting innovation).
Further, many digital health investments have faced issues achieving effective return on investment in part
due to the significant fragmentation of data and digital assets within and across health systems. This
fragmentation has made it challenging to have comprehensive trusted information about individuals for the
provision of care. Fragmentation also complicates data compilation and aggregation – with appropriate
protections – for health system safety and performance, public health, research, and innovation among
others. These challenges slow effective health interventions, prevent scientific advancement, and increase
costs due to unnecessary investments in data collection, storage, and integration. To realise the benefits
of AI, it is critical to strengthen data and digital foundations so that data are representative of populations
and the capacity exists to use AI solutions at scale.
What this includes: There are many considerations for data in healthcare, including that it is findable,
accessible, interoperable, and reusable (FAIR) (GO FAIR, 2021[7]). Enablers for the use of health data
could be operationalised for data to be:
• Findable: Data assets intended for analytic use could be catalogued and easily discoverable by
authorised users. Catalogues should be annotated to support the search of data assets for re-use.
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Improving the findability of high quality, representative datasets will support AI developers,
researchers, and policymakers to access existing resources, reduce redundant investments, and
speed up innovation. This is especially important in the current landscape of international and
cross-border collaborations, in areas of public health, research and healthcare innovations.
• Accessible with individual consent: Establish clear practices for individuals to consent for the
use of their health data in care and analytics. Consider the appropriate use of opt-in or opt-out
consent, or the use of compliant alternatives when consent is impractical while achieving health
system objectives (such as individual emergency care or emergency public health management).
• Accessible health data for primary use: Health data should be accessible to authorised
healthcare providers, individuals, and/or their delegates for direct care, aligned with individual
consent and considerations for privacy and safety. How data are held and governed should also
be addressed while shifting culture from risk avoidance and protection to data stewardship, which
prioritises optimising the collection, use, and re-use of data in individual and public interest while
providing appropriate protections across data supply chains. This process supports integrated
care, professional collaboration, and patient trust (Digital Transformations for Health Lab, 2025[8]).
• Accessible health data for analytic use in the public interest (. public safety, health system
improvement): Data intended for analytic uses can be accessible through clear, streamlined
processes that minimise administrative barriers while protecting data privacy and security.
Facilitating streamlined access across health institutions supports the responsible use of data in
the public interest in research, public health initiatives, and innovation. These could be governed
by robust data protection and accountability frameworks that support trusted cross-border
collaboration. A key tool in this policy area is consideration for privacy enhancing technologies
(PETs) (. pseudonymisation, encryption) (OECD, 2025[9]; OECD, 2023[10]). Decisions regarding
data access and analytic use should follow transparent, accountable processes, with clearly
defined criteria – supported by mechanisms that demonstrate how confidentiality, intellectual
property and commercially sensitive information will be protected throughout the data lifecycle
across the public and private sectors.
• Interoperable: Health systems should be interoperable. This does not mean that every country
adopts the same standards for data capture and exchange, rather, it means that the standards
that are adopted are compatible with other standards in the areas that are most important for the
provision of care and crucial analytic uses such as research, clinical trials, or public health.
International co-operation between standards bodies, governments, and cross sector stakeholders
is beneficial to enabling interoperability. Governments could support this by adopting globally
recognised standards, along with technologies that enable data exchange across diverse systems
while accommodating local needs.
• Data quality: Health system data should have its quality measured and reported. Given that data
are the foundation of AI, if data are of poor quality, then the resulting AI solutions will be of poor
quality. Measuring and reporting data quality will help international collaborators work together with
confidence while building trust with stakeholders. This quality evaluation would measure areas of
skewness, representativeness, accuracy, traceability, and timeliness. Consideration could
also be given to whether data quality measures are globally aligned to support international
collaboration, while still allowing for local components that reflect specific population needs.
• Linkable: National health systems can adopt a consistent privacy preserving digital identifier
to securely link data about individuals regardless of the location or mode of their care, while
safeguarding their privacy. This will help people who receive care across borders to increase trust
and ensure completeness of their health records. Linking data is also critical for AI development,
where linking data across domains strengthens the quality, representativeness, and
trustworthiness of AI solutions (OECD, 2025[11]).
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• Sovereign and Secure Storage: National health systems can clarify expectations for the security
and storage of data, including how data are encrypted – both in motion and at rest – and where
data are stored to protect data sovereignty. This helps protect sovereign interests while
supporting international collaboration in critical areas such as digital security.
Emerging leading practices: The enablers above fall under the umbrella of health data governance,
though there is variation in how these are implemented. The European Health Data Space (EHDS) and
the identification of country-led health data authorities is a strong step forward. As countries work together
to align their national practices with the EHDS regulation, they will help to define the leading practices.
Some emerging leading practices such as the adoption of the International Patient Summary (IPS) for
patient records demonstrates how international collaboration could operate with a foundation of open
standards and interoperability (The International Patient Summary, 2025[12]). In addition, the Digital Health
Model Security Notice developed by the Global Digital Health Partnership (GDHP) provides consistent
requirements to vendors of digital security expectations (Global Digital Health Partnership, 2023[13]).
Enabler: Define and operationalise the assurance and use of AI in health services
Why this is important: The OECD AI Principles (OECD, 2024[4]) provide clarity on what is expected for
the deployment and use of AI across the entire AI lifecycle. Specifically, that AI solutions should advance
Inclusive Growth, Human Rights, Transparency and Explainability, Security and Safety, and
Accountability. While these terms are often used to describe trustworthy AI, incompatibilities in the
interpretation of these terms can prevent the scalability of AI solutions when they need to be adjusted to
meet different requirements.
For innovative AI solutions in health to scale across broader populations, co-ordination of assurance and
approval processes is essential. This can be supported through cross-country agreements on shared
principles, compatible assurance standards, and transparent evaluation procedures. Such co-ordination
does not imply that approval in one country automatically results in approval in others, but rather reflects
the principle of “reliance”, where assurance processes are designed to be compatible. This approach
enables AI innovators to understand the steps required to gain approval, facilitating the scaling of solutions
across borders. It also helps reduce duplication, accelerate access, and foster economic growth.
What this includes: Enablers for the AI life cycle can be clarified to enable:
• AI solution risk management: A key component of AI assurance processes includes the
assessment of the risk of the AI solutions in the context of the health system. Questions such as
“what is the likelihood of a negative occurrence?”, “what is the likelihood of not achieving a positive
occurrence if we do not act?”, “what is the impact of negative or positive occurrences?”, and “what
actions are in place that lower negative likelihoods, increase positive likelihoods, and minimise
negative impacts” are all considerations. Compatibility for risk management – and the
identification of appropriate actions given the risk level – help innovators understand what
controls need to be incorporated into their solutions to provide greater certainty in their design. This
also helps health stakeholders build trust in the use of AI solutions through the implementation of
controls.
• AI solution assessment: Health systems can clarify the processes for the assessment of AI
solutions – establishing requirements for AI solutions to be considered, reviewed, and accepted as
an approved solution. The assessment should include the classification of what constitutes AI as
a medical device in health vs. AI as a non-medical device in health and criteria for their review
and approval. This assessment also includes the intended use for the AI solution in terms of its
geography, target population, and other factors. This could include direct-to-consumer vs. direct-
to-provider vs. administrative solutions. Review would assess the cost-effectiveness and other
implications of AI solutions including through market-accepted tools such as Health Technology
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Assessments (HTA). Health systems could also align on tool effectiveness, value assessment
and reimbursement, and ongoing compliance frameworks to promote the long-term viability of
integrating AI into clinical practice and encourage investment in scalable, sustainable innovation.
Given that AI solutions are frequently updated, based on training data, the criteria for periodic
updates to and review of the AI solutions could be defined.
• Algorithmic useability: Assure that AI solutions are fair in their application, ideally with reduced
skewness, or at minimum understood representativeness of the data used to develop the
solution and its alignment with anticipated used of the AI solution. Further, AI solutions should
provide sufficiently informative output to help users understand the AI decisions depending on the
context of the AI solutions and individuals either using or impacted by the solution to facilitate their
use of results from the AI solution (. explainable and transparent).This facilitates having a
human-in-the-loop during the use of AI in health.
• AI Model cards: Health systems could have methods to clearly communicate the result of the
assessment to inform consumers prior to deciding to use the AI solutions. A model card, a
standardised document summarising key information about an AI solution, including its
intended use, performance, limitations and risks, can help achieve this. This would include details
on skewness, performance metrics and representativeness, as well as transparent disclosure of
any residual skewness, the reasons it persists, and its acceptability for specific subpopulations.
Given the adaptive nature of AI systems, model cards should be treated as living documents,
updated regularly to reflect model changes, retraining and real-world performance over time.
International collaboration is useful to articulate their trustworthiness for the consumer and health
professionals alike.
• Procurement: Health system procurement could be modernised to leverage established networks
to simplify procurement for individual health facilities (. through certification models). New
procurements could be updated to articulate the requirements of publicly funded health systems
as new technologies, such as AI, are introduced into their footprint. This could include areas noted
in the previous sections around data management and security (including interoperability),
adoption of model cards, algorithmic viability, and assurance models. A modern approach to
procurement helps amplify a consistent signal to the vendor community of expectations and
requirements.
Alongside assurance and procurement, additional considerations are:
• Patient consent for AI in clinical use: Define when consent for the use of AI is necessary (vs.
appropriate alternatives) and how that consent is received, recorded, and shared.
• Approval for AI in clinical use: Guidelines can be provided to health systems with
recommendations on when AI solutions could be used due to the availability of evidence, safety
requirements, and appropriate evaluations. These recommendations would be contingent on
contextual factors such as patient characteristics and their point along the care pathway. This
guidance aims to clarify when human oversight is required and when AI solutions may be
autonomous with appropriate review.
• Reimbursement of AI in clinical use: Clarify when the use of such solutions require special
re-imbursement (as opposed to being a tool in the delivery or care), (Lobig et al., 2023[14]), (van
Kessel et al., 2023[15]).
• Liability for AI in clinical use: Define how liability is managed, such as how to address a
negative situation that arises from the use of the AI solution. Could be extended to consider both
direct-to-clinician and direct-to-consumer models.
To accelerate innovation, many countries are establishing approaches for:
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• Regulatory sandboxes: Regulatory sandboxes test innovative solutions that are on the “edge” of
current practices. The sandbox provides extraordinary oversight to prevent harms, while
establishing a “proving ground” for what is innovation possible with regulatory change or clearer
interpretation. Implementation of a regulatory sandbox includes adjusting approval processes post-
test to avoid lengthy delays for critical technologies while sufficient evidence is gathered.
Emerging leading practices: A leading practice is the adoption of model cards – via Conformité
Européenne (CE) marking in Europe, which certifies compliance with the European Union (EU) safety,
health, and environmental protection standards (European Commission, 2025[16]) and the Coalition for
Health AI (CHAI) in the United States which has introduced model card frameworks to enhance
transparency and accountability in health-related AI systems (CHAI, 2025[17]). The International Medical
Devices Regulatory Forum (IMDRF) is currently in the process of designing a comprehensive guide to AI-
regulation across the AI lifecycle (IMDRF, 2024[18]).
Areas where more convergence is needed: Countries are developing idiosyncratic approaches to risk
management and the definition of medical devices. For example, in the United States, the FDA has
adopted risk management practices based on the output of the AI solution whereas the European Union
(EU) considers both the input and output of AI solutions. This divergence has led to cases where AI tools
like the AI Scribes were originally considered high risk in the EU (due to using personal health information
for training) and low risk in the United States (due to doctors being in the loop). These differences highlight
a key challenge for international collaboration, that without the shared understanding of risk categories
policy coherence and scalability of AI solutions remains limited. Establishing agreement on such risk
categories is a necessary pre-condition for developing interoperable AI risk governance and scalability of
AI solutions, perhaps leveraging the risk management framework for medical devices published by the
International Medical Devices Regulator Forum (IMDRF) (IMDRF, 2025[19]; Chapman, 2025[20]).
Enabler: Sufficient human and technical capacity and capability
Why this is important: Given the wide range of policy and technical activities that are needed to help the
design, implementation, operation, monitoring, and improvement of AI in health solutions – and the
significant opportunity from the responsible scale of AI – there is a need to build human and technical
capacity. Human capacity and capability are important across borders to support the transferability and
mobility of skills. Technical capacity is beneficial to share sufficient and available computing power.
What this includes: To build sufficient and capable human and technical capacity, countries may establish
practices for:
• Planning for future workforce: Consider the needs of the health workforce as part of the
integration of AI: what are the impacts on the frontline? What are the impacts on patients? What
supporting functions are necessary? and What oversight functions are required for the system to
be safe? There is benefit to proactively planning and acting on future health workforce needs, for
example considering future hybrid roles. Planning could assess current gaps in tools and
knowledge across different workforce functions. Targeted upskilling programmes can be
developed to minimise knowledge gaps and prepare the digitally enabled health workforce.
• Planning for public knowledge needs and trust: Consider the needs for the public as AI is
introduced into the health system. Focusses on building sufficient knowledge to enable the public’s
capacity to effectively engage with AI. This planning process recognises that trust and
understanding are mutually reinforcing. Planning could assess the current gaps in tools and
knowledge across the public and include developing methodologies, approaches and training
programmes to prepare the public to be empowered participants in their care with AI.
• Planning for sufficient technical capacity: The development of AI solutions requires sufficient
computing power and storage to operate effectively. Decisions would need to be made about
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whether to insist on local computing capability or whether co-operation is better to share computing
capacity with partners, for example with cloud computing and enabling federated learning. Planning
could assess current gaps in technical capacity, project future demand, and develop forward-
looking strategies for building technical capacity when and where it is needed.
Emerging leading practice: Few countries in the OECD actively project and plan for the health workforce
that is required for the integration of AI into health. In that work, new planning could include technologists,
data managers and stewards, privacy officers, policymakers, as well as administrative staff and office
managers. In the United Kingdom, the Digital and Data Profession Capability Framework has been
developed to build capacity in support functions, though is not specific to the health sector (,
2025[21]). By doing workforce planning co-operatively the definition of new roles and evolution of existing
roles will be more consistent which will simplify accreditation and transferability of health workers.
Guardrail: Operationalise action toward common vision and objectives
Why this is important: Health systems benefit from having an agreed common objective for the design
of their AI in health ecosystem that is agreed across all stakeholder groups. An agreed common objective
helps decisions about priority and action to evaluate those decisions against their contribution to the agreed
common objective. Across countries, specific agreed objectives will vary; however, they will likely adopt
common attributes in being person-centred and rights-based (to health, to privacy, and to benefit from
science).
What this includes: Responsible scale of AI into health systems benefit from common objectives defined
by:
• Establishing a vision: Governments could clearly articulate the intended outcome from
investments in AI for health. This could consider impacts on people, providers, and industry – and
the role of government to guide actions to achieve the vision. Based on the OECD Health Minister
Declaration (2024), health systems should be centred on people and provide personalised care
(OECD/LEGAL/0500, 2024[22]; WHO, 2021[23]). Health systems would preserve and enhance
human-decision making while promoting human and societal well-being.
• Establishing a strategy or action plan: As will be seen in Chapter 3, countries are starting to
develop specific strategies or action plans at the intersection of AI and health to deliver against
agreed objectives for AI in health. Such strategies direct collective actions toward balancing the
forces of market, health, and the public.
Many of the strategies or action plans adopt common ways of working that could be considered:
• Open: Open approaches include where data, digital, and AI infrastructure are as open as possible
and closed as necessary. This approach fosters protection and the use of health data resources
for systems outcomes. This will help systems have fair access to data while minimising skewness
and discrimination. Humans benefit from AI solutions that are trained on representative data sets.
• Collaborative: Stakeholders co-operate to share knowledge to achieve better health outcomes
(WHO, 2021[23]). Systems also work together toward the responsible scale of trustworthy AI
(OECD, 2024[4]). In health, this helps private and public sector organisations have collaboration
mechanisms that are inclusive, transparent, and open and result in better compatibility of
approaches within and across borders. This collaboration will encourage the involvement of
stakeholders, including patients, the public, and underserved communities, and foster transparent
and understandable processes for AI solutions to effectively scale and be responsibly adopted.
• Responsive and adaptable: Policies and processes are designed to be responsive and adaptable
to new opportunities and risks as they arise. Speed of change is calibrated to the risk of action and
risk of inaction. Given the increasing pace of innovation, this may require forms of adaptive
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governance such as rules being set dynamically by designated authorities with appropriate
transparency, communication, and oversight rather through specific regulation or legislation.
Responsiveness also understands the range of types of AI solutions (. LLMs, agentic AI,
predictive analysis, etc.) and using a risk-based evaluation, helps to prioritise action to respond to
the size of the risk or opportunity.
Emerging leading practice: As will be discussed in Chapter 3, an emerging leading practice is for health
systems to develop a strategy or action plan at the intersection of AI and health to ensure progress that is
in alignment with common health system objectives.
Guardrail: Oversight, measurement, and monitoring of AI solutions
Why this is important: A key method to assure the ongoing safety and effectiveness of AI in health
solutions is to measure their performance in clinical settings and to monitor those metrics to act when
certain thresholds are attained. When measurement approaches are aligned across jurisdictions, multi-
country assessments and co-operation become more feasible. More broadly, consistent measurement –
alongside open sharing of results – simplifies post-market assessment, supporting collaborative learning
and improvement. This would be grounded in governance structures supported by empowered institutions.
Performance measurement also supports the tracking of benefits from the use of AI in health and progress
against strategic milestones.
What this includes: Measurement and monitoring of AI solutions would include:
• Evaluating post-market effectiveness: In the approval for the AI solution, there is generally an
effectiveness measure that describes the expected clinical performance and measures to
verify the stability of the AI solution both before and after deployment. Post-market
effectiveness tracks the AI solution for performance drift and establishes pathways for notification
and remediation. Measurement plans and reporting cycles post-deployment may include safety
plans (reviewing for unintended and harmful outcomes), patient-reported experience and outcomes
and socio-demographic uptake and impacts. That is, measuring for the availability, accessibility,
use, and impact of the AI solution by populations. Serious incidents could be transparently
reported and acted on to prevent future re-occurrence.
• Benefits assessment: This would measure and estimate the net benefit from the AI solution. This
would be related to the benefits hypothesis and/or quantified targets developed at the approval of
the AI solution. Benefits could span the quintuple aim, including aspects of outcome improvement,
equity uplift, or system efficiency. Benefits would be related to system planning and re-investment,
including benefits from the scale of approved AI solutions. For example, this could estimate
changes in workforce productivity and help understand how that productivity gain is re-invested –
whether in reducing burnout, investing more time in care, and / or in seeing additional patients.
• Energy usage: This would measure the energy impact and usage of AI in health solutions to
understand the environmental impact of solutions from energy used in the end-to-end life cycle
of AI processing. This includes evaluating future energy demands during development,
deployment, and operation, and considering the availability of supporting energy sources. As AI
solutions scale, understanding and managing their environmental impact becomes increasingly
critical to responsible scale.
Governance of AI solutions would include:
• Appointing a Champion: Health systems could identify a champion for AI – a human leader with
passion and leadership to mobilise stakeholders across the sector, that ensures enablers for
AI are working as intended, resolving areas of conflict, identifying methods to scale, and sharing
leading practices. The Champion could be supported by a series of governance structures
designed to oversee the AI in health programme of work.
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• Establish accountability and control: To clarify what roles are important to guide the responsible
scale of AI solutions and how those roles interact. This would identify clear responsibility for
actions and decision making, including which roles are appropriately involved and consulted. It
also includes defining controls for analytic uses of data and use of AI solutions – across public
health, clinical trials, research, innovation, and health system management. This applies to all
stakeholders – across the public, providers, industry, and policymakers among others.
• Establishing incentives to collaborate: Health systems can adopt incentives to encourage
collaboration with AI solutions and the use of health data. This would also establish dis-incentives
to significantly penalise individuals or organisations who abuse their position to either affect health
data privacy breaches or deliberating fragmenting the AI in health ecosystem contrary to agreed
objectives.
Emerging leading practices: For post-market surveillance, the OECD has developed an incident
reporting framework (OECD, 2025[24]). For further measurement and monitoring, the OECD has developed
the Catalogue of Tools and Metrics for Trustworthy AI, including safety, privacy, and fairness (OECD,
2025[25]). In addition, HealthAI is creating a Global Regulatory Network that will include incident reporting
(HealthAI, 2026[26]). As work progresses, there will be further opportunities for collaboration on leading
practices for governance, measurement, monitoring, and response.
Engagement: Public
Why this is important: The public and the public’s ability to achieve good health outcomes are at the
heart of the health system. The public are both the source of most data that are used in AI solutions and
the means to measure AI’s effectiveness. As in the OECD Recommendation on Health Data Governance
(OECD, 2016[27]), the first component involves the public being involved in the design, implementation, and
operation of health data governance solutions (Vanstone et al., 2023[28]). As such, the public should be
engaged in all stages of the design, deployment, operation, and improvement of AI in health solutions.
To support meaningful engagement, efforts can also be made to promote digital and AI literacy. Without
this, public feedback may be less informed, as such, less valuable, and the public may be less willing to
participate. Engagement must also reflect the diversity of the population to ensure inclusive perspectives.
Some communities are structurally underserved and may require tailored engagement strategies to ensure
their needs and perspectives are reflected in the co-production of AI solutions. Additionally, Indigenous
communities hold distinct rights, including in data governance and digital technologies, which must be
respected and fulfilled in accordance with relevant legal and ethical frameworks.
Cross-border co-operation enables countries to draw on shared insights. Because incidents that
undermine trust in one jurisdiction may influence perceptions in others, co-ordinated approaches can help
maintain and strengthen public trust.
What this includes: To engage the public, this includes:
• Trust: While trust is critical for the responsible scale of AI in health, there is not yet a consistent
definition of what trust requires (Starke et al., 2025[29]). An approach for fostering trust involves
engaging with the public to learn what it takes for AI to be trustworthy. This would include
transparency in practices and open communication to demonstrate trustworthiness and checking
with the public when their requirements for trustworthiness change. Trust can also be strengthened
through multistakeholder engagement, ensuring diverse perspectives are included in governance.
• Educate to empower: Improving the awareness, level of digital and AI literacy, and
understanding of AI in health will help demystify the new solutions for individuals and allow them
to make informed decisions. With the growth of direct-to-consumer access of AI solutions, the
public are becoming key actors in their care journey. A focus on education would empower people
to be aware of the availability (and trustworthiness) of solutions and how they fit in their overall
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programme of health and care. This will need to be done along with providers, carefully and
respectfully, as this may result in changing the legacy patient-provider dynamic.
• Representativeness: Engagement with the public in all areas above should be designed to be
representative – respectful of diversities across cultures, languages, age, and other appropriate
socio-demographic factors. In the case of Indigenous Peoples, engagement should respect their
data sovereignty, . Canada’s First Nations Peoples of Ownership, Control, Access, and
Possession) (FNIGC, 2025[30]); and Australia’s Framework for Governance of Indigenous Data
(Commonwealth of Australia, 2024[31])). Working with Indigenous peoples should also respect
CARE principles (Collective benefit, Authority to control, Responsibility, and Ethics) (GIDA,
2018[32]). When considering representativeness, there may be value in respecting both individual
and collective rights for trustworthy and inclusive AI in health (Alelyani, 2025[33]).
Emerging leading practice: There are several emerging leading practices associated with this area.
France has established a Citizen Assembly for Digital Health where diverse members of the public are
engaged to discuss issues pertinent to AI and digitalisation of health and making recommendations to the
government (Agence du Numérique en Santé, 2025[34]). For education, there are emerging programmes
such as Elements of AI, which originated in Finland in 2019, provides free AI education for the public
(Elements of AI, 2025[35]).
Engagement: Health providers
Why this is important: Health providers are essential to the functioning of the health system. They serve
as a trusted guide to help their patients navigate their health journey. Like the public, it is appropriate for
health providers to be involved in all stages of the design, deployment, operation, and improvement of AI
in health solutions. As with the public, there is value in being co-operative across borders and learn from
each other and build trustworthiness with the medical community.
What this includes: Engagement of health providers that personally provide care to their patients mirrors
that of the public. As such, this includes:
• Trust: Fostering trust involves organisations learning what it takes to be trustworthy,
demonstrating that trustworthiness, and checking with health providers when their requirements
for trustworthiness change. This is in a continuous, iterative process. Involvement of stakeholders,
specifically health providers at every stage of the AI lifecycle is also crucial for building and
sustaining trust.
• Educate to empower: Improving the awareness and understanding of AI in health will help to
demystify these new solutions for providers to make informed decisions and effectively
integrate those solutions into their care delivery workflows. Many AI in health solutions are direct-
to-provider. In these cases, health providers should be aware of the reliability and appropriate use
parameters of these solutions. Importantly, they should also know how to effectively query and
challenge AI outputs when information is inconsistent with their knowledge (Babic et al., 2021[36]).
Along with the public, this should be done carefully and respectfully, as this may alter traditional
patient-provider dynamics.
• Clinical adoption: Beyond technical assistance and financial investment, adopting AI in
healthcare includes clinical workflow redesign, interdisciplinary co-ordination, and support
for leadership structures that can guide responsible implementation. Sustained change
management efforts should also address cultural readiness and fostering trust in using AI-enabled
tools, supported by transparent communication and clear accountability for outcomes.
Emerging leading practice: There are a number of leading practices in this area, with France mandating
AI and digital health training across all health professional programmes starting in the 2025-2026 academic
year (Comité interministériel de l’Intelligence artificielle, 2025[37]), Estonia incorporating AI training in all
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medical school curricula (TI-Hüpe, 2025[38]), and Germany offering certified AI training through its national
KI-Campus platform (KI-Campus, 2022[39]). These examples highlight varying, ongoing efforts to engage
and co-ordinate AI capacity building of healthcare providers.
Engagement: Industry
Why this is important: Industry is a central pillar in the development of AI in health. They are innovators
and solution developers to help advance the efficiency and effectiveness of health systems. Industry
engagement can aid in understanding existing barriers and opportunities with policy and regulation,
co-investing in evidence generation, and co-creating meaningful solutions that meet the needs of the health
system today. It is appropriate for industry partners, which include pharmaceutical and medical technology
companies, small, medium and large sized enterprises in health and technology, academia and research
institutions, to be involved in all stages of the design, deployment, operation, and improvement of AI in
health solutions.
What this includes: Engagement of industry aligns with that of the public and health providers. As such,
this includes:
• Trust: Building and maintaining trust requires collaboration to actively cultivate trustworthiness
through transparent practices and involve industry through evolving changes. This would focus on
predictability of process and minimising unnecessary duplication. Involvement of industry along the
AI lifecycle is beneficial for building and sustaining trust.
• Partnership: Developing practices to establish public-private partnerships including the
parameters around such partnerships – such as how risks are managed, intellectual property is
protected, and how value from the partnership is shared. Could include consideration for
embedding members of the public in such partnerships.
Emerging leading practice: There are a number of emerging leading practices in this area, in France,
the PECAN programme is offering one-year of reimbursement for digital medical devices while finalising
clinical evidence to obtain certification (G_NIUS, 2025[40]). The United Kingdom has also established the
NHS AI lab and associated regulatory sandbox to engage, educate and enable new solutions to enter the
health ecosystem (NHS England, 2025[41]). In Australia, the Sparked is actively engaging with industry to
enhance interoperability and the foundations for future AI implementations (Sparked, 2025[42]). In
Catalonia (Spain), the Observatory of Artificial Intelligence in health provides a transparent repository of
all AI tools used in the region, and support for new initiatives to enter the system and scale (AI Observatory
in Health, 2025[43]).
Trustworthiness: Responsible Use of AI in health
Why this is important: With AI, humans should feature first and foremost above machines when
determining what is ultimately useful for the good of humans. The foundation of this rests in an ethical
approach. Within biomedical ethics there are four key principles, as first defined in 1979: autonomy,
beneficence, non-maleficence (. ‘do not harm the patient), and justice. (Varkey, 2020[44]). When
implementing AI in healthcare, there is an ethical imperative to question and consider the bioethical
principles and subsequent ethical impacts of the AI solution.
This ethical imperative extends beyond the clinical setting to overall system decisions about how AI is
evaluated, scaled, and governed. There are several ethical principles which can be considered by
policymakers, industry, and implementers when embedding ethics into standard procedures. One such
guide comes from the OECD AI principles, which provide a global reference point, signalling for AI systems
to be inclusive and beneficial to people and the planet; respectful of human rights and democratic values;
transparent and explainable; robust, secure, and safe; and accountable (OECD, 2024[4]). These principles
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reinforce the importance of embedding ethics throughout the entire AI lifecycle, from design, deployment
to its use.
As AI is becoming integrated into health systems worldwide, it is critical that countries define ethical
principles in alignment with their own societal values, legal frameworks, and healthcare priorities. Countries
may develop their own guidelines or adopt internationally recognised tools such as OECD AI principles
(OECD, 2024[4]), WHO’s guiding principles for ethical use of AI in health (WHO, 2024[45]), or UNESCO’s
recommendation on the Ethics of AI (UNESCO, 2021[46]). These principles guide policymakers and
implementers across the AI lifecycle ensuring there are clear ethical guidelines and certainty across the
end-to-end decision making process.
What this includes: Ethical frameworks, principles, and concepts to permeate AI solutions across their
lifecycle:
• Ethical development, deployment, and use of AI solutions aligned with applicable legal,
regulatory, and societal expectations including boundaries for non-AI development. Provision
should be made for human oversight and decision making for scenarios that are not clearly
addressed by ethical guidelines. The objective could be to clarify as much as possible to provide
certainty for end-to-end processes while aligning with bioethical principles.
• Bioethical assessment should consider how patient beneficence, patient non-maleficence,
patient autonomy, and justice are incorporated into AI solution development, adoption, and scale.
For AI solutions intended for the market, the AI solution manufacturer, developer, researcher (and
similar) may state how their specific AI solution is aligned with established bioethical
principles while quantifying impacts on people and populations with respect to cost-savings,
time-savings, or other meaningful benefits.
Emerging leading practices: International organisations and governments are moving towards the
development and use of ethical impact assessment of AI solutions. In New Zealand, Health NZ has
established a National Artificial Intelligence and Algorithm Expert Advisory Group (NAIAEAG) as a leading
governance mechanism for the ethical and safe use of AI in health. By requiring all AI projects involving
Health NZ data or deployment to be registered with the group, and through the use of a structured AI
checklist, NAIAEAG ensures that proposed tools meet ethical, technical, clinical and operational standards
(Health New Zealand/ Te Whatu Ora, 2025[47]). Taking a different approach, Korea has positioned itself as
a global leader in AI governance by integrating ethical oversight into its national legislation. As part of its
forthcoming AI Act, expected to be launched in 2026, Korea will establish a national AI committee, chaired
by the President and supported by a network of advisory sub-committees. With a five-year mandate, the
Committee is tasked with ensuring that AI governance remains adaptable to technological advancements.
The legislation will promote a decentralised ethics model, encouraging public and private sector
organisations to establish independent AI ethics committees. (JustAI and Singhal, 2025[48]; Kim & Chang,
2024[49]).
Areas where more convergence is needed: There have been many publications on the ethics of AI, with
over 140 documents being published across 19 countries – from governmental organisations,
governments, and industry – including aspects of fairness, explainability, and transparency without a clear
consensus on requirements (Korukoglu et al., 2025[50]). Ethical guidelines and principles are areas that
would benefit from specific action to determine what international consensus is both beneficial and
necessary to facilitate the scalability of trustworthy AI in health solutions.
Using the AI in Health Policy Checklist
The above sections have described the policy areas that experts have identified as benefiting from
conscious decisions about how they will operate in health systems. These policy areas have been
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highlighted as they also benefit from consistency within jurisdictions and compatibility across jurisdictions.
This approach fosters local autonomy while simplifying the ability to scale solutions. The following table
(see Table ) summarises the nine policy areas that make up the checklist for AI in health and suggests
key questions to consider for the consistent development of each policy area.
Table . Policy checklist to support responsible scale and scale of AI in health
Type Policy Areas Policy Concept Consideration – including assuring compatibility across borders were appropriate
Enablers Better use of
health data
Findable Are there activities that support the findability of health data assets? With national data
catalogues?
Consent for health
data
Is there clarity for when consent for health data use is necessary (vs. appropriate alternatives)
and how consent is managed?
Accessible:
Primary care
Are comprehensive health data available for the provision of direct patient care?
Accessible:
Analytic use
Are health data available (with protection) for system analytics, public health, research, or
innovation with defined controls?
Interoperable Are there activities to incentivise and enable the interoperability of data assets?
Data quality Are there activities to ensure the integrity of data across organisations? Including timeliness
of data for its purpose?
Linkable Are there digital identifiers that support linking across data sets?
Sovereign/secure
storage
Are there activities for the secure storage of data assets? Do they include consideration for
(indigenous) data sovereignty?
Enabling the
use of AI
Risk management Are there frameworks for assessment and management of risk associated with AI solutions?
Technical
Assessment
Are there frameworks for the technical classification and assessment of AI solutions?
Including update of features or algorithms?
Algorithmic
useability
Are there frameworks to assessment the useability of AI solutions with respect to fairness,
explainability, and transparency?
Model cards Are there frameworks in use for “model cards” to communicate the result from assessments
of AI solutions?
Procurement Are there frameworks in use for the procurement of AI in health solutions?
Consent for AI use
in care
Is there clarity for when expressed consent for the use of AI is necessary? And how that
consent is managed?
Approval for AI
use in care
Is there an approach that defines boundaries for the clinical use of AI?
Reimbursement
for AI
Is there clarity on how and when use of AI solutions is re-imbursed and how much?
Liability for AI use
in care
Is there clarity on how liability is managed in care settings?
Regulatory
sandboxes
Are there frameworks for the implementation of regulatory sandbox(es) to accelerate
innovation?
Building
capacity and
capability
Workforce
capacity
Are there actions to develop the evolving health workforce including front-office, back-office
and oversight roles?
Public capacity Are there actions to build public digital and AI literacy?
Technical capacity Are there actions for future technical capacity, including storage and computing power?
Guardrails Oversight,
measurement
and monitoring
Post-market
effectiveness
Are there actions to measure post-market effectiveness and address issues / incidents as
they arise?
Benefit
assessment
Are there actions to measure benefits from the implementation of the AI solution (including
consideration for productivity change)?
Energy usage Are there actions to measure the energy usage to effectively understand and plan for
environmental impact and energy demand?
Champion /
oversight
Is there a designated AI champion for the responsible integration of AI into health systems?
Supported by an oversight body?
Accountability Is there clarity on end-to-end accountability and control across the AI life cycle across all
identified roles?
Incentives Are there incentives in place for collaborative development of AI and use of health data? And
penalties for mis-use?
Agreed
objectives
Establish a vision Is there an endorsed vision for the intended target state?
Establish an
action plan
Is there an endorsed strategy or action plan to achieve the agreed vision?
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Type Policy Areas Policy Concept Consideration – including assuring compatibility across borders were appropriate
Open Is there consideration for open data and AI solutions as part of the action plan?
Collaborative Is there consideration for collaboration as part of the action plan? Does this include cross-
border collaboration?
Responsive /
Adaptable
Is there consideration for policies and processes to be responsive / adaptable to risk or
opportunity as part of the action plan?
Engagement Public Trust Are there programmes in place for building trustworthiness of AI solutions with the public?
Educate to
empower
Are there programmes in place to educate and empower the public on the use of AI?
Representative Are programmes designed to address cultural, linguistic, age, and other diversities? Are
considerations for Indigenous Peoples?
Health
Providers
Trust Are there programmes in place for inclusive design with providers?
Educate to
empower
Are there programmes in place to educate and empower providers on the use of AI?
Clinical adoption Are there programmes to optimise adoption of AI tools by providers?
Industry Trust Are there programmes in place for transparent and inclusion of industry in end-to-end AI life
cycle processes?
Partnership Are there frameworks to establish public-private partnerships?
Trust Trustworthy AI Ethical
development
Is there an approach that defines processes to manage ethical considerations for AI
solutions? Including boundaries?
Bioethics
assessments
Are there considerations for the alignment with bioethical principles in the development and
use of AI?
Note: Bolded items are included in the analysis in Chapter 3.
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