Yves Saint James Aquino , Wendy A. Rogers , Susannah Louise Sage Jacobson , Bernadette Richards , Nehmat Houssami , Maame Esi Woode , Helen Frazer , Stacy M. Carter
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This study aims to identify the challenges in and opportunities for the regulation of adaptive features of AI.</p></div><div><h3>Materials and Methods</h3><p>We performed in-depth qualitative, semi-structured interviews with a diverse group of 72 experts in high-income countries (Australia, Canada, New Zealand, US, and UK) who are involved in the development, acquisition, deployment and regulation of healthcare AI systems.</p></div><div><h3>Results</h3><p>Our findings revealed perceived challenges in the regulation of adaptive features of machine learning (ML) systems. These challenges include the complexity of AI applications as products subject to regulation; lack of accepted definitions of adaptive changes; diverse approaches to defining significant adaptive change; and lack of clarity about regulation of adaptive change. Our findings reflect potentially competing interests among different stakeholders and diversity of approaches from regulatory bodies and legislators in different jurisdictions across the globe. In addition, our findings highlight the complex regulatory implications of adaptive AI that differ from traditional medical products, drugs or devices.</p></div><div><h3>Conclusion</h3><p>The perceived regulatory challenges raised by adaptive features of AI applications require high-level coordination within a complex regulatory ecosystem that consists of medical device regulators, professional accreditation agencies, professional medical organisations, and healthcare service providers. Regulatory approaches should complement existing safety protocols with new governance mechanisms that specifically take into account the variety of roles and responsibilities that will be required to monitor, evaluate and oversee adaptive changes.</p></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"13 3","pages":"Article 100892"},"PeriodicalIF":3.4000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211883724000558/pdfft?md5=e77c781da3bba485a99425116cd1b1d9&pid=1-s2.0-S2211883724000558-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Defining change: Exploring expert views about the regulatory challenges in adaptive artificial intelligence for healthcare\",\"authors\":\"Yves Saint James Aquino , Wendy A. 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This study aims to identify the challenges in and opportunities for the regulation of adaptive features of AI.</p></div><div><h3>Materials and Methods</h3><p>We performed in-depth qualitative, semi-structured interviews with a diverse group of 72 experts in high-income countries (Australia, Canada, New Zealand, US, and UK) who are involved in the development, acquisition, deployment and regulation of healthcare AI systems.</p></div><div><h3>Results</h3><p>Our findings revealed perceived challenges in the regulation of adaptive features of machine learning (ML) systems. These challenges include the complexity of AI applications as products subject to regulation; lack of accepted definitions of adaptive changes; diverse approaches to defining significant adaptive change; and lack of clarity about regulation of adaptive change. Our findings reflect potentially competing interests among different stakeholders and diversity of approaches from regulatory bodies and legislators in different jurisdictions across the globe. In addition, our findings highlight the complex regulatory implications of adaptive AI that differ from traditional medical products, drugs or devices.</p></div><div><h3>Conclusion</h3><p>The perceived regulatory challenges raised by adaptive features of AI applications require high-level coordination within a complex regulatory ecosystem that consists of medical device regulators, professional accreditation agencies, professional medical organisations, and healthcare service providers. Regulatory approaches should complement existing safety protocols with new governance mechanisms that specifically take into account the variety of roles and responsibilities that will be required to monitor, evaluate and oversee adaptive changes.</p></div>\",\"PeriodicalId\":48672,\"journal\":{\"name\":\"Health Policy and Technology\",\"volume\":\"13 3\",\"pages\":\"Article 100892\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2211883724000558/pdfft?md5=e77c781da3bba485a99425116cd1b1d9&pid=1-s2.0-S2211883724000558-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Policy and Technology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2211883724000558\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH POLICY & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Policy and Technology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211883724000558","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
Defining change: Exploring expert views about the regulatory challenges in adaptive artificial intelligence for healthcare
Objective
Continuously learning or adaptive artificial intelligence (AI) applications for screening, diagnostic and other clinical services are yet to be widely deployed. This is partly due to existing device regulation mechanisms that are not fit for purpose regarding the adaptive features of AI. This study aims to identify the challenges in and opportunities for the regulation of adaptive features of AI.
Materials and Methods
We performed in-depth qualitative, semi-structured interviews with a diverse group of 72 experts in high-income countries (Australia, Canada, New Zealand, US, and UK) who are involved in the development, acquisition, deployment and regulation of healthcare AI systems.
Results
Our findings revealed perceived challenges in the regulation of adaptive features of machine learning (ML) systems. These challenges include the complexity of AI applications as products subject to regulation; lack of accepted definitions of adaptive changes; diverse approaches to defining significant adaptive change; and lack of clarity about regulation of adaptive change. Our findings reflect potentially competing interests among different stakeholders and diversity of approaches from regulatory bodies and legislators in different jurisdictions across the globe. In addition, our findings highlight the complex regulatory implications of adaptive AI that differ from traditional medical products, drugs or devices.
Conclusion
The perceived regulatory challenges raised by adaptive features of AI applications require high-level coordination within a complex regulatory ecosystem that consists of medical device regulators, professional accreditation agencies, professional medical organisations, and healthcare service providers. Regulatory approaches should complement existing safety protocols with new governance mechanisms that specifically take into account the variety of roles and responsibilities that will be required to monitor, evaluate and oversee adaptive changes.
期刊介绍:
Health Policy and Technology (HPT), is the official journal of the Fellowship of Postgraduate Medicine (FPM), a cross-disciplinary journal, which focuses on past, present and future health policy and the role of technology in clinical and non-clinical national and international health environments.
HPT provides a further excellent way for the FPM to continue to make important national and international contributions to development of policy and practice within medicine and related disciplines. The aim of HPT is to publish relevant, timely and accessible articles and commentaries to support policy-makers, health professionals, health technology providers, patient groups and academia interested in health policy and technology.
Topics covered by HPT will include:
- Health technology, including drug discovery, diagnostics, medicines, devices, therapeutic delivery and eHealth systems
- Cross-national comparisons on health policy using evidence-based approaches
- National studies on health policy to determine the outcomes of technology-driven initiatives
- Cross-border eHealth including health tourism
- The digital divide in mobility, access and affordability of healthcare
- Health technology assessment (HTA) methods and tools for evaluating the effectiveness of clinical and non-clinical health technologies
- Health and eHealth indicators and benchmarks (measure/metrics) for understanding the adoption and diffusion of health technologies
- Health and eHealth models and frameworks to support policy-makers and other stakeholders in decision-making
- Stakeholder engagement with health technologies (clinical and patient/citizen buy-in)
- Regulation and health economics