This section collects references to the most significant technological developments in the use of AI in medicine and healthcare.
AHEAD - Technological evolution
Scientific paper - Mapping the susceptibility of large language models to medical misinformation across clinical notes and social media: a cross-sectional benchmarking analysis
Anno 2026
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Benchmarking study evaluating how susceptible LLMs are to medical misinformation across clinical-note and social-media contexts.
Scientific paper - “Women do not have heart attacks!” Gender Biases in Automatically Generated Clinical Cases in French
Anno 2025
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Study of gender bias in automatically generated French clinical cases, showing over-generation of male cases and mismatch with real disorder prevalence.
Scientific paper - Data Poisoning Vulnerabilities Across Healthcare AI Architectures: A Security Threat Analysis
Anno 2025
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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.
Scientific paper - The limits of fair medical imaging AI in real-world generalization
Anno 2024
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This paper explores how artificial intelligence systems designed for medical imaging are capable of indirectly learning demographic information about patients (gender, age, race) and using it as “shortcuts” in diagnoses. This finding compromises the quality of diagnoses for certain groups and indicates unpredictable behavior in fairness metrics when these models are used in hospitals with a patient population different from the one in which the model was trained. Although methods for correcting these biases by removing these variables have been proposed, there is no definitive solution to this problem, as this data remains essential in many clinical contexts and requires a detailed assessment by the medical team based on the specific case.
Tool of evaluation - T4D (Thinking for Doing) dataset
Anno 2023
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Dataset derived from ToMi for evaluating whether LLMs can use Theory-of-Mind inferences to choose appropriate actions.
Scientific paper - On Evaluation Metrics for Medical Applications of Artificial Intelligence
Anno 2022
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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.
Scientific paper - A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Anno 2021
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This paper explains the potential biases that may arise throughout the lifecycle of artificial intelligence systems, taking an iterative approach to the processes of data generation, model development, and deployment.
We provide a definition of the typical issues associated with each stage and a specific mitigation strategy.
Pubblicato il: Lunedì, 14 Aprile 2025
