Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?

• Published in: Cell reports. Medicine 2022-05, Vol.3 (5), p.100622, Article 100622
• Main Author: London, Alex John
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; bias; bioethics; equity; healthcare; Humans; learning health systems; Medicine; Patient Care; research ethics; social determinants of health; social value; structural injustice
• ISSN: 2666-3791; 2666-3791
• DOI: 10.1016/j.xcrm.2022.100622

There is considerable enthusiasm about the prospect that artificial intelligence (AI) will help to improve the safety and efficacy of health services and the efficiency of health systems. To realize this potential, however, AI systems will have to overcome structural problems in the culture and practice of medicine and the organization of health systems that impact the data from which AI models are built, the environments into which they will be deployed, and the practices and incentives that structure their development. This perspective elaborates on some of these structural challenges and provides recommendations to address potential shortcomings. [Display omitted] Alex John London argues that AI is unlikely to promote the learning necessary to ensure that therapeutic intent translates into beneficial patient outcomes until it overcomes structural challenges in medicine’s knowledge ecosystem. He identifies these challenges and provides recommendations to improve AI’s ability to bridge important healthcare knowledge gaps.