• Chris Paton and Shinji Kobayashi.
• University of Oxford, UK.
• Yearb Med Inform. 2019 Aug 1; 28 (1): 47-51.

ObjectivesArtificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an "open science" approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI systems.MethodIn response to the call for participation for the 2019 International Medical Informatics Association (IMIA) Yearbook theme issue on AI in healthcare, the IMIA Open Source Working Group conducted a rapid review of recent literature relating to open science and AI in healthcare and discussed how an open science approach could help overcome concerns about the adoption of new AI technology in healthcare settings.ResultsThe recent literature reveals that open science approaches to AI system development are well established. The ecosystem of software development, data sharing, education, and research in the AI community has, in general, adopted an open science ethos that has driven much of the recent innovation and adoption of new AI techniques. However, within the healthcare domain, adoption may be inhibited by the use of "black-box" AI systems, where only the inputs and outputs of those systems are understood, and clinical effectiveness and implementation studies are missing.ConclusionsAs AI-based data analysis and clinical decision support systems begin to be implemented in healthcare systems around the world, further openness of clinical effectiveness and mechanisms of action may be required by safety-conscious healthcare policy-makers to ensure they are clinically effective in real world use.Georg Thieme Verlag KG Stuttgart.

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