Evaluating Artificial Intelligence in Clinical Settings-Let Us Not Reinvent the Wheel

• Main Authors: Kathrin Cresswell (Author), Nicolette de Keizer (Author), Farah Magrabi (Author), Robin Williams (Author), Michael Rigby (Author), Mirela Prgomet (Author), Polina Kukhareva (Author), Zoie Shui-Yee Wong (Author), Philip Scott (Author), Catherine K Craven (Author), Andrew Georgiou (Author), Stephanie Medlock (Author), Jytte Brender McNair (Author), Elske Ammenwerth (Author)
• Format: Book
• Published: JMIR Publications, 2024-08-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1438-8871; 10.2196/46407
• Keywords: Computer applications to medicine. Medical informatics; R858-859.7; Public aspects of medicine; RA1-1270
• Source: Journal of Medical Internet Research, Vol 26, p e46407 (2024)

Given the requirement to minimize the risks and maximize the benefits of technology applications in health care provision, there is an urgent need to incorporate theory-informed health IT (HIT) evaluation frameworks into existing and emerging guidelines for the evaluation of artificial intelligence (AI). Such frameworks can help developers, implementers, and strategic decision makers to build on experience and the existing empirical evidence base. We provide a pragmatic conceptual overview of selected concrete examples of how existing theory-informed HIT evaluation frameworks may be used to inform the safe development and implementation of AI in health care settings. The list is not exhaustive and is intended to illustrate applications in line with various stakeholder requirements. Existing HIT evaluation frameworks can help to inform AI-based development and implementation by supporting developers and strategic decision makers in considering relevant technology, user, and organizational dimensions. This can facilitate the design of technologies, their implementation in user and organizational settings, and the sustainability and scalability of technologies.