This section collects references to the most significant technological developments in the use of AI in medicine and healthcare.
This section collects references to the most significant technological developments in the use of AI in medicine and healthcare.
Benchmarking study evaluating how susceptible LLMs are to medical misinformation across clinical-note and social-media contexts.
Study of gender bias in automatically generated French clinical cases, showing over-generation of male cases and mismatch with real disorder prevalence.
This paper analyses data poisoning vulnerabilities in healthcare AI systems, showing that a small number of malicious samples can compromise models across architectures, highlighting risks for safety, robustness, and clinical reliability.
Dataset derived from ToMi for evaluating whether LLMs can use Theory-of-Mind inferences to choose appropriate actions.
This paper examines how the performance of AI-based medical decision tools is measured, showing that common metrics can be misleading and that clinicians need multiple complementary measures to properly evaluate these systems.