Other possible perspectives for solving the negative outcome penalty paradox in the application of artificial intelligence in clinical diagnostics

Bibliographic Data

ID21514904
AuthorsHongnan Ye (0009-0004-9013-2919, Beijing Alumni Association of China Medical University, Beijing, China, corresponding author)
Year2025
Volume51
Issue1
Pages57-58
Publication date2025-01-01
Open AccessNo
TypeARTICLE
VenueJournal of Medical Ethics (JOURNAL)
Journal identifiersISSN: 0306-6800 • E-ISSN: 1473-4257
PublisherBMJ (PUBLISHER • GB)
DOI10.1136/jme-2024-109968
PMID38871400
OpenAlexW4399622980
LanguageEN
References cited9

Artificial intelligence (AI), represented by machine learning, artificial neural networks and deep learning, is impacting all areas of medicine, including translational research (from bench to bedside to health policy), clinical medicine (including diagnosis, treatment, prognosis and healthcare resource allocation) and public health. At a time when almost everyone is focused on how to better realise the promise of AI to transform the entire healthcare system, Dr Appel calls for public attention to the AI in medicine and the negative outcome penalty paradox. Proposing this topic has deepened our thinking about the application of AI in clinical diagnostics, and also prompted us to find more effective ways to integrate AI more effectively into future clinical practice. In addition to Dr Appel’s insightful advice, I hope to offer three other possible perspectives, including changing public perceptions, re-engineering clinical practice processes and introducing more stakeholders, to further the discussion on this topic

Applications of artificial intelligence · Clinical Practice · Health care · Political science · Artificial Intelligence in Healthcare and Education · Computer Science · Ethics in Clinical Research · Healthcare cost, quality, practices · Mathematics · Medicine · Nursing · Psychology · Artificial Intelligence

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