✉news HealthMedicine first seen 9 h ago, last 7 h ago, peak #31
Deep Learning in Medicine Faces Explainability-Privacy Trade-off
Original: Deep Learning in Medicine Hits a Wall: Explainability and Privacy Are Locked in Tension
A new analysis argues that deep learning in medicine has hit a structural wall: the demand for explainable models and the obligation to protect patient privacy are fundamentally in tension. Techniques that make AI decisions transparent can expose sensitive data, while strong privacy protections make models harder to interpret, leaving clinicians and regulators with a difficult trade-off.
Why now: Ongoing debate over deploying AI in healthcare, where doctors and regulators demand both transparency and data protection.
deep learningmedicinepatient privacy
Evidence
- Deep Learning in Medicine Hits a Wall: Explainability and Privacy Are Locked in Tension · Bioengineer.org
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