Defining change: Exploring expert views about the regulatory challenges in adaptive artificial intelligence for healthcare

IF 3.4 3区 医学 Q1 HEALTH POLICY & SERVICES
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引用次数: 0

Abstract

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.

定义变革:探索专家对医疗保健领域适应性人工智能监管挑战的看法
目标用于筛查、诊断和其他临床服务的持续学习或自适应人工智能(AI)应用尚未得到广泛应用。部分原因是现有的设备监管机制不适合人工智能的自适应功能。本研究旨在确定人工智能自适应功能监管所面临的挑战和机遇。材料与方法我们对高收入国家(澳大利亚、加拿大、新西兰、美国和英国)参与医疗人工智能系统开发、获取、部署和监管的 72 位专家进行了深入的半结构式定性访谈。结果我们的研究结果显示了机器学习(ML)系统自适应功能监管所面临的挑战。这些挑战包括:人工智能应用作为受监管产品的复杂性;缺乏公认的适应性变化定义;定义重大适应性变化的方法多种多样;对适应性变化的监管缺乏明确性。我们的研究结果反映了不同利益相关者之间潜在的利益冲突,以及全球不同司法管辖区监管机构和立法者的方法多样性。此外,我们的研究结果还强调了适应性人工智能与传统医疗产品、药品或设备不同的复杂监管影响。结论人工智能应用的适应性特征所带来的监管挑战需要在复杂的监管生态系统中进行高层协调,该生态系统由医疗设备监管机构、专业认证机构、专业医疗组织和医疗服务提供商组成。监管方法应通过新的治理机制来补充现有的安全协议,这些机制应特别考虑到监测、评估和监督适应性变化所需的各种角色和责任。
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来源期刊
Health Policy and Technology
Health Policy and Technology Medicine-Health Policy
CiteScore
9.20
自引率
3.30%
发文量
78
审稿时长
88 days
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
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