Trustworthy Medical Question Answering: An Evaluation-Centric Survey

Medical AI systems need evaluation across multiple trustworthiness dimensions: The paper identifies six critical areas—Factuality, Robustness, Fairness, Safety, Explainability, and Calibration—that must all be assessed to ensure medical question-answering systems are truly reliable, rather than focusing on just one aspect like accuracy.

Current LLM deployment in healthcare outpaces safety verification: As large language models become integrated into medical settings, their responses directly influence clinical decisions and patient outcomes, yet comprehensive trustworthiness evaluation remains incomplete, creating a gap between deployment speed and safety assurance.

Specific techniques can improve medical AI reliability: The survey identifies evaluation-guided improvement methods including retrieval-augmented grounding (connecting answers to verified sources), adversarial fine-tuning (testing systems against challenging scenarios), and safety alignment (training systems to prioritize patient safety) as practical approaches to enhance trustworthiness.

Expert evaluation remains a critical bottleneck: The paper highlights "scalable expert evaluation" as an open challenge, indicating that verifying medical AI systems currently requires significant involvement from healthcare professionals, which limits how quickly and widely these systems can be properly validated.

Real-world testing gaps threaten patient safety: The survey identifies the lack of "real-world deployment studies" as a major gap, meaning medical QA systems are often evaluated in controlled settings but their actual performance and safety in clinical practice remains largely unknown.