# CONRep: Uncertainty-Aware Vision-Language Report Drafting Using Conformal Prediction

arXiv: https://arxiv.org/abs/2602.03910 · HTML: https://mlsanity.com/paper/2602.03910/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Can VLMs Reliably Assess Sidewalk Accessibility Attributes from Pedestrian-Level Imagery?](https://mlsanity.com/paper/2609.17882/) (2026)

## Abstract (arXiv)

Automated radiology report drafting (ARRD) using vision-language models (VLMs) has advanced rapidly, yet most systems lack explicit uncertainty estimates, limiting trust and safe clinical deployment. We propose CONRep, a model-agnostic framework that integrates conformal prediction (CP) to provide statistically grounded uncertainty quantification for VLM-generated radiology reports. CONRep operates at both the label level, by calibrating binary predictions for predefined findings, and the sentence level, by assessing uncertainty in free-text impressions via image-text semantic alignment. We evaluate CONRep using both generative and contrastive VLMs on public chest X-ray datasets. Across both settings, outputs classified as high confidence consistently show significantly higher agreement with radiologist annotations and ground-truth impressions than low-confidence outputs. By enabling calibrated confidence stratification without modifying underlying models, CONRep improves the transparency, reliability, and clinical usability of automated radiology reporting systems.