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

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 prac...

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Bibliographic Details
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
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Summary: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.
ISSN:2666-3791
2666-3791
DOI:10.1016/j.xcrm.2022.100622