为健康领域的人工智能赢得信任：一条协作前进的道路

W H I T EP A P E R Images:Getty Images Contents Foreword Executive summary Introduction 1Empowering trustworthy AI in health: The urgent need for collaboration 1.1Global divergences challenge the scaling of AI in health1.2The private sector is key to driving progress and standardization 2The need for a pragmatic approach: Guidelines, sandboxes andpost-market surveillance 2.1Legislation can build a strong baseline for governing AI in health 2.2Sandboxes provide a safe space in which the privatesector can innovate 2.3Post-market surveillance can help cope withthe evolving nature of AI 3The importance of public–private partnerships for AI in health 3.1The role of public–private partnerships in regulatingmedical devices, including software 3.2Private-sector capabilities can help test andoperationalizethe regulatory process 3.3Quality assurance resources: An approach to PPPsfor independent testing and training Conclusion Appendix: A selection of regulatory sandbox inititatives Contributors Acknowledgements Endnotes Disclaimer This document is published by theWorld Economic Forum as a contributionto a project, insight area or interaction.The findings, interpretations andconclusions expressed herein are a resultof a collaborative process facilitated andendorsed by the World Economic Forumbut whose results do not necessarilyrepresent the views of the World Economic Foreword Andy MooseHead of Health andWellness, Centre for Ben HornerManaging Director Equally important is strengthening technicalcapacity among regulators, innovators andhealthcare leaders to develop a sharedunderstanding of AI’s capabilities and risks.Public–private partnerships should be positionedat the core of this transformation – co-developing Artificial intelligence (AI) holds great promise totransform healthcare – enhancing diagnostics,optimizing workflows and improving healthoutcomes for all. However, realizing AI’s benefitsresponsibly demands a fundamental evolution Existing evaluation frameworks – built for productsthat remain typically unchanged after approval,such as pharmaceuticals and medical devices –are not fully equipped to manage the dynamic,evolving nature of AI technologies. The probabilistic If we act now, we can embed trust in thefoundations of digital health transformation. Byaligning innovation with ethical principles andfocusing on continuous evaluation, AI can fulfil itspromise: improving health outcomes, enhancing To manage these challenges effectively, regulatorymodels must evolve. Dynamic governancemechanisms such as regulatory sandboxes, life-cycle evaluation and post-market monitoring willbe essential to ensure that AI systems remain safe, Executive summary AI will reshape healthcare, but realizingits full potential requires responsible Healthcare systems globally face growing pressures:rising costs, workforce shortages and persistentinefficiencies. In this context, AI offers transformativeopportunities to enhance patient outcomes andoptimize system performance. However, realizing –Independent quality assurance resourcesand real-world testing environments, suchas those being developed under initiativeslike the Testing and Experimentation Facility 3.Promote public–private collaboration Today’s medicine regulatory frameworks – largelydesigned for pharmaceuticals and medicaldevices – are not fully suited to manage theprobabilistic, dynamic nature of AI technologies.Traditional evaluation methods, which emphasize –Public–private partnerships (PPPs) shouldmove beyond consultation to active co- –Such collaboration is vital to ensure thatregulatory practices keep pace with AIinnovation while safeguarding patient trust This paper also emphasizes the importance ofglobal coordination. Divergences in AI regulatoryapproaches across regions – especially betweenthe Global North and Global South – risk creatingbarriers to the scalable deployment of AI in This paper, developed through a collaborationbetween the World Economic Forum’s Centre forHealth and Healthcare and Boston Consulting 1.Address fragmentation and build Ultimately, the future of AI in healthcare mustbe grounded in adaptability, transparency andshared responsibility. By strengthening evaluationprocesses, building technical capacity and fosteringstructured public–private collaboration, health –Current AI ecosystems are fragmented,and many health leaders lack a deep –Health systems must build technical literacyamong decision-makers to critically assess 2.Adapt evaluation and regulatory frameworks The path forward demands continuous innovationnot only in technology but also in regulation andsystem design. The time to act is now, to ensure –New approaches, such as regulatorysandboxes, post-market surveillance and –Guidelines must complement legislation toenable innovation while maintaining high Introduction Building a trustworthy health AI ecosystemdemands new regulatory models, continuous Healthcare expenditure has been risi