Avoiding common failures in AI for health and medicine.

Reliability failures in AI for healthcare include erroneous outputs, population disparities, and performance decline, threatening safe deployment across 20+ failure modes.

• Why it matters: Ensuring AI reliability is critical for safe, effective healthcare delivery, yet current solutions often fall short in addressing complex, real-world challenges and variability.
• What they did: The review analyzes 20+ failure modes in predictive and generative AI, evaluates existing mitigation approaches, and highlights the limitations of current technical solutions.
• The result: Findings emphasize the need for lifecycle-aware evaluation, ongoing monitoring, and governance to improve AI reliability and support trustworthy healthcare applications.