mlsanityData to 14 Oct 2026

Confidence Calibration for Multimodal LLMs: An Empirical Study through Medical VQA

arXiv 2606.19950 ↗First posted 18 Jun 2026cs.CV cs.AIcited by 4

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

Multimodal Large Language Models (MLLMs) show great potential in medical tasks, but their elicited confidence often misaligns with actual accuracy, potentially leading to misdiagnosis or overlooking correct advice. This study presents the first comprehensive analysis of the relationship between accuracy and confidence in medical MLLMs. It proposes a novel method that combines Multi-Strategy Fusion-Based Interrogation (MS-FBI) with auxiliary expert LLM assessment, aiming to improve confidence calibration in Medical Visual Question Answering (VQA). Experiments demonstrate that our method reduces the Expected Calibration Error (ECE) by an average of 40% across three Medical VQA datasets, significantly enhancing MLLMs' reliability. The findings highlight the importance of domain-specific calibration for MLLMs in healthcare, offering a more trustworthy solution for AI-assisted diagnosis.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 17 of its 27 references that are in the map. First posted on arXiv 18 Jun 2026.

Builds on

17 of 27 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning26 Feb
  2. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day1 Jun

Other references · 15

  1. 2017On Calibration of Modern Neural Networkscited by 4,960
  2. 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
  3. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  4. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  5. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 812
  6. 2021SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answeringcited by 449
  7. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 401
  8. 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 256
  9. 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 121
  10. 2024What Large Language Models Know and What People Think They Knowcited by 110
  11. 2024Learning Dynamics of LLM Finetuningcited by 95
  12. 2024When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentationcited by 59
3 more
  1. 2024When to Trust LLMs: Aligning Confidence with Response Qualitycited by 43
  2. 2023Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledgecited by 32
  3. 2024Calibrating Verbalized Probabilities for Large Language Modelscited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026CARE: Confidence-Aware Reasoning for Reliable Medical VQAcited by 0
  2. 2026Uncertainty-Aware Decision Making in Multimodal Large Language Modelscited by 0
  3. 2026EVADE: Evidence-Verified Agentic Diagnosis with Escapecited by 0
  4. 2026OmniMed-Jev: Calibrating LVLM Confidence for Trustworthy Medical Multimodal Decisions via System Onecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Just how sure are you? Improving Verbalized Uncertainty Calibration in Medical VQA
  2. 2026Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
  3. 2024Enhancing Healthcare LLM Trust with Atypical Presentations Recalibration
  4. 2024SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA Tasks
  5. 2024MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models
  6. 2024Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models
  7. 2024ACE-M3: Automatic Capability Evaluator for Multimodal Medical Models
  8. 2025Mind the Gap: Benchmarking LLM Uncertainty and Calibration with Specialty-Aware Clinical QA and Reasoning-Based Behavioural Features