OmniMed-Jev: Calibrating LVLM Confidence for Trustworthy Medical Multimodal Decisions via System One
cs.LG
Submitted: 2026-09-30
Updated: 2026-09-30
Code: https://github.com/lytang63/OmniMed-Jev
Sources
- HealthBench: Evaluating Large Language Models Towards Improved Human Health
- Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
- A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
- this-that-model-1.0: A typed decision model that decides in 30 ms, for a millionth of a cent
- Universal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains
- Jev for Scientific Decisions: Evaluating Semantic Choices and Their Consequences
- Cascaded Language Models for Cost-effective Human-AI Decision-Making
- CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine
- Can Jev Judge Radiology Reports? Evaluating a System One Model for Clinical Factuality
- Evaluating Decision Models for Text Annotation in Computational Social Science
- Selective Prediction Reduces the Negative Effects of Automation Bias Overall but Increases False Negatives
- Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents
- Medical Hallucinations in Foundation Models and Their Impact on Healthcare
- Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
- JEV-as-a-Judge: Accept When Confident, Escalate When Unsure
- ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities
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