1. Prudently Evaluating Medical Adaptive Machine Learning Systems.
- Author
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Kuersten, Andreas
- Subjects
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RISK assessment , *COMPUTERS , *DATA analysis , *DEBATE , *PATIENT safety , *PRIVACY , *PATIENT care , *GOAL (Psychology) , *MOTIVATION (Psychology) , *ETHICS , *MEDICAL research , *MACHINE learning , *LEARNING strategies , *HEALTH care industry , *MEDICAL practice , *MEDICAL ethics - Abstract
The article focuses on the debate surrounding the classification of medical adaptive machine learning systems (MAMLS) as research tools versus clinical tools. It arguing against the stringent regulatory burdens proposed by Sparrow and colleagues; the ethical implications of generalizable data in healthcare; the role of continuous learning in improving patient care and the need for a balanced approach to regulatory oversight that prioritizes safety and efficacy over overly cautious restrictions.
- Published
- 2024
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