arXiv:2607. 20582v1 Announce Type: cross Abstract: Machine learning models for medical image analysis typically lack a reliable measure of confidence, limiting their use in ambiguous or atypical cases.
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arXiv:2603. 24481v2 Announce Type: replace Abstract: Miscalibrated confidence scores are a practical obstacle to deploying AI in clinical settings.
arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.
arXiv:2607. 16317v1 Announce Type: cross Abstract: Deep networks now subtype brain tumors on MRI about as well as specialist readers, yet accuracy is not what keeps them out of the clinic.
The paper introduces egRUE, an explainable uncertainty estimation method that merges uncertainty quantification with feature‑level explanations for medical AI predictions. egRUE incorporates prediction explanations into its uncertainty calculation and decomposes uncertainty into contributions from individual features. Experiments and a user study with medical experts show that egRUE improves reliability, interpretability, and calibrated trust compared to existing methods.
The study investigates how confidence intervals (CIs) behave in medical imaging AI by analyzing 24 segmentation and classification tasks with 19 models per task, various metrics, aggregation strategies, and CI methods. It finds that required sample sizes for reliable CIs vary widely, CI behavior depends on performance metrics, aggregation strategy, and problem type, and that different CI methods differ in reliability and precision. The authors provide a decision tree to guide researchers in selecting appropriate CI methods, aiming to support future consensus guidelines on reporting performance uncertainty.
The paper introduces MedDream, a radiographic world model that learns a shared continuous latent state from paired chest radiograph-text observations. MedDream outperforms existing diagnostic and generative AI models across eight clinical datasets, improving diagnostic reasoning, resident concordance, and evidence generation. Targeted synthetic augmentation guided by subgroup performance gaps further enhances model performance, particularly for Asian patients.