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CONRep: Uncertainty-Aware Vision-Language Report Drafting Using Conformal Prediction
Danial Elyassirad1, Benyamin Gheiji1, Mahsa Vatanparast1
1Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Journal of Imaging Informatics in Medicine
|August 10, 2026
Summary
This study introduces CONRep, a framework to quantify uncertainty in vision-language model (VLM)-based automated radiology report drafting (ARRD). CONRep enhances ARRD reliability by distinguishing certain from uncertain outputs, improving clinical integration.
Area of Science:
- Artificial Intelligence in Medical Imaging
- Natural Language Processing for Healthcare
- Radiology Informatics
Background:
- Automated radiology report drafting (ARRD) using vision-language models (VLMs) shows promise but requires robust uncertainty quantification for clinical trust.
- Current VLM-based ARRD systems lack mechanisms to reliably differentiate high-confidence from low-confidence outputs, hindering safe deployment.
- Quantifying predictive uncertainty is crucial for enhancing the transparency and reliability of AI tools in diagnostic radiology.
Purpose of the Study:
- To develop and evaluate CONRep, a novel framework for quantifying uncertainty in VLM-based ARRD.
- To assess the impact of uncertainty stratification on the performance and reliability of ARRD systems.
- To support the trustworthy clinical deployment of VLM-based ARRD by enhancing transparency and safety.
Main Methods:
- Developed CONRep in two settings: label-based (ChestX-Det10 dataset) and sentence-based (Open-I dataset).
- Applied conformal prediction (CP) to stratify VLM outputs into certain and uncertain subgroups.
- Evaluated performance using area under the receiver operating characteristic curve (AUROC) for classification and cosine similarity for text generation, corroborated by an independent large language model (LLM).
Main Results:
- Across both pipelines, outputs classified as certain demonstrated significantly higher agreement with ground truth compared to uncertain outputs.
- In label-based experiments, the certain subset achieved significantly higher AUROCs (P < 0.05).
- In sentence-based experiments, certain cases showed significantly greater semantic similarity (P < 0.001), corroborated by LLM evaluation (P < 0.001).
Conclusions:
- CONRep provides a model-agnostic framework for uncertainty-aware ARRD using conformal prediction.
- Quantifying predictive uncertainty enhances the transparency, reliability, and clinical usability of VLM-based ARRD systems.
- CONRep supports safer integration of AI into radiology workflows by providing trustworthy confidence estimates.
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