arXiv:2606.27023v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) applied to Medical Visual Question Answering (VQA) tend to produce overconfident outputs regardless of actual correctness, and existing verbalized confidence calibration methods, developed primarily for text only LLMs, do not account for the multimodal nature of medical image understanding. This work proposes a training based framework that finetunes MLLMs to improve their calibration using a composite loss function combining a Brier style calibration term, an anchor regularizer that prevents confidence co
The increasing deployment of MLLMs in critical applications like medical VQA necessitates improved reliability and trustworthiness, pushing researchers to directly address issues like overconfidence and poor calibration.
Improving the calibrated uncertainty of AI models in domains such as healthcare is crucial for their safe and effective integration, directly impacting trust, diagnostic accuracy, and patient outcomes.
This R&D represents a step towards more trustworthy and deployable AI systems in critical applications by directly tackling the challenge of verbalized uncertainty calibration for multimodal models.
- · Healthcare providers
- · Patients
- · AI ethicists
- · MLOps platforms
- · Uncalibrated MLLMs in medical settings
- · Developers ignoring calibration
Medical AI diagnostics become more reliable and trustworthy, accelerating their adoption.
Increased legal and regulatory scrutiny on MLLM calibration standards for critical applications.
The development of 'confidence-aware' AI assistants that proactively highlight their own uncertainties to human collaborators.
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Read at arXiv cs.LG