Wolff Justus, Pauling Josch, Keck Andreas, Baumbach Jan
Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.
Syte - Strategy Institute for Digital Health, Hamburg, Germany.
Front Digit Health. 2021 Jun 16;3:594971. doi: 10.3389/fdgth.2021.594971. eCollection 2021.
Artificial Intelligence (AI) in healthcare has demonstrated high efficiency in academic research, while only few, and predominantly small, real-world AI applications exist in the preventive, diagnostic and therapeutic contexts. Our identification and analysis of success factors for the implementation of AI aims to close the gap between recent years' significant academic AI advancements and the comparably low level of practical application in healthcare. A literature and real life cases analysis was conducted in Scopus and OpacPlus as well as the Google advanced search database. The according search queries have been defined based on success factor categories for AI implementation derived from a prior World Health Organization survey about barriers of adoption of Big Data within 125 countries. The eligible publications and real life cases were identified through a catalog of in- and exclusion criteria focused on concrete AI application cases. These were then analyzed to deduct and discuss success factors that facilitate or inhibit a broad-scale implementation of AI in healthcare. The analysis revealed three categories of success factors, namely (1) policy setting, (2) technological implementation, and (3) medical and economic impact measurement. For each of them a set of recommendations has been deducted: First, a risk adjusted policy frame is required that distinguishes between precautionary and permissionless principles, and differentiates among accountability, liability, and culpability. Second, a "privacy by design" centered technology infrastructure shall be applied that enables practical and legally compliant data access. Third, the medical and economic impact need to be quantified, e.g., through the measurement of quality-adjusted life years while applying the CHEERS and PRISMA reporting criteria. Private and public institutions can already today leverage AI implementation based on the identified results and thus drive the translation from scientific development to real world application. Additional success factors could include trust-building measures, data categorization guidelines, and risk level assessments and as the success factors are interlinked, future research should elaborate on their optimal interaction to utilize the full potential of AI in real world application.
人工智能（AI）在医疗保健领域的学术研究中已展现出高效率，但在预防、诊断和治疗等实际应用场景中，真正的人工智能应用却很少，且大多规模较小。我们对人工智能实施成功因素的识别与分析，旨在弥合近年来人工智能在学术领域取得的重大进展与医疗保健领域相对较低的实际应用水平之间的差距。我们在Scopus、OpacPlus以及谷歌高级搜索数据库中进行了文献和实际案例分析。根据世界卫生组织此前针对125个国家大数据采用障碍所做调查得出的人工智能实施成功因素类别，确定了相应的搜索查询。通过一套聚焦具体人工智能应用案例的纳入和排除标准目录，确定了符合条件的出版物和实际案例。然后对这些案例进行分析，以推导和讨论促进或阻碍人工智能在医疗保健领域广泛实施的成功因素。分析揭示了三类成功因素，即（1）政策制定，（2）技术实施，以及（3）医疗和经济影响评估。针对每一类因素都得出了一系列建议：首先，需要一个经过风险调整的政策框架，区分预防原则和无许可原则，并区分问责、责任和罪责。其次，应应用以“设计即隐私”为核心的技术基础设施，以实现实际且符合法律规定的数据访问。第三，需要对医疗和经济影响进行量化，例如通过在应用CHEERS和PRISMA报告标准时测量质量调整生命年。私营和公共机构如今已可基于已确定的结果利用人工智能实施，从而推动从科学发展到实际应用的转化。其他成功因素可能包括建立信任措施、数据分类指南和风险水平评估，由于这些成功因素相互关联，未来研究应详细阐述它们的最佳相互作用，以在实际应用中充分发挥人工智能的潜力。
