Medical image models can be persistently wrong under image degradation
Beyond the Evidence Boundary: Behavioral Mirage Under Progressive Degradation in Medical VLMs
Computer Vision and Pattern RecognitionComputation and Language
Summary
Medical AI models are often tested only on clear images, but in real medical settings, images can get worse gradually. The authors studied how models behave when images become harder to interpret and found that some models keep giving confident answers even when they are likely wrong, a situation called a mirage. They developed a way to track how reliable a model’s answers remain as image quality drops and found that typical confidence measures don’t always reveal when a model is failing. This means medical AI systems need new ways to recognize when they should stop answering or indicate uncertainty.
What this means in practice
- •For medical ai engineers: Improve testing frameworks to detect when medical image models provide unreliable answers as image quality degrades.
- •For clinical ai deployment teams: Develop safeguards that trigger abstention or uncertainty signals when medical AI models reach evidence boundaries in degraded images.
Authors
Edric Castel C. Hao, Rez Samantha Z. Floresca
Abstract
VLMs for medical image interpretation are typically evaluated on clean inputs, despite the progressive evidence loss that can occur in high stakes clinical settings. Existing robustness studies show that performance can degrade while confidence remains high, but do not establish when a previously reliable model-case pair ceases to behave reliably. We introduce a baseline-anchored framework that traces baseline-correct cases through progressive image degradation to identify an empirical evidence boundary, and define mirage as continued commitment at this boundary, regardless of correctness. Across three VLMs on three medical datasets, only MedGemma provides a sufficiently large baseline-correct pool for trajectory analysis. On VQA-RAD, the only dataset in which the question text varies, language-only answerability is associated with baseline correctness (AUROC 0.677) and with commitment at the boundary (AUROC 0.653 to 0.670). MedGemma's post-boundary behavior is heterogeneous, including direct mirage, correct recovery, mirage dissolution, consistent abstention, and oscillation, with mirage rates of 16.0 to 100.0% across dataset-modality pairs. Despite this behavioral heterogeneity, residual-stream probes decode commit-versus-abstain behavior at the boundary with AUROC of 0.895 to 0.994 under nested cross-validation. These results show that progressive evidence loss can produce persistent commitment that output entropy and abstention behavior do not consistently reflect, motivating boundary-aware evaluation, option-order controls, and improved abstention mechanisms for reliable medical foundation models operating under partial observation.