Limited bedside impact of medical imaging AI stems from structural misalignment with clinical decision-making across six dimensions, not mainly from weak algorithms, regulation, or explainability.
Artificial Intelligence in Healthcare: Lost In Translation?
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abstract
Artificial intelligence (AI) in healthcare is a potentially revolutionary tool to achieve improved healthcare outcomes while reducing overall health costs. While many exploratory results hit the headlines in recent years there are only few certified and even fewer clinically validated products available in the clinical setting. This is a clear indication of failing translation due to shortcomings of the current approach to AI in healthcare. In this work, we highlight the major areas, where we observe current challenges for translation in AI in healthcare, namely precision medicine, reproducible science, data issues and algorithms, causality, and product development. For each field, we outline possible solutions for these challenges. Our work will lead to improved translation of AI in healthcare products into the clinical setting
fields
physics.med-ph 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing
Limited bedside impact of medical imaging AI stems from structural misalignment with clinical decision-making across six dimensions, not mainly from weak algorithms, regulation, or explainability.