REVIEW 4 cited by
ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https://rexrank.ai, a public leaderboard and challenge for assessing AI-powered radiology report generation. Our framework incorporates ReXGradient, the largest test dataset consisting of 10,000 studies, and three public datasets (MIMIC-CXR, IU-Xray, CheXpert Plus) for report generation assessment. ReXrank employs 8 evaluation metrics and separately assesses models capable of generating only findings sections and those providing both findings and impressions sections. By providing this standardized evaluation framework, ReXrank enables meaningful comparisons of model performance and offers crucial insights into their robustness across diverse clinical settings. Beyond its current focus on chest X-rays, ReXrank's framework sets the stage for comprehensive evaluation of automated reporting across the full spectrum of medical imaging.
Forward citations
Cited by 4 Pith papers
-
CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement
A chest X-ray VLM co-trained with classification and grounding heads, tuned with DAPO reinforcement learning, and augmented with deterministic measurement tools outperforms prior radiology VLMs on report generation, V...
-
Scaling medical imaging report generation with multimodal reinforcement learning
UniRG-CXR, a Qwen3-VL-8B model trained with SFT plus GRPO reinforcement learning that directly optimizes the ReXrank metric components, reports state-of-the-art 1/RadCliQ-v1 results on all four ReXrank chest X-ray dat...
-
Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays
Domain-adapted LLM encoders trained with masked token prediction and supervised contrastive learning improve chest X-ray image-text retrieval and external generalization, reaching GREEN scores of 0.308 on MIMIC-CXR an...
-
From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine
A PRISMA-ScR scoping review of 144 studies finds the field shifting from text-only LLMs to multimodal AI in medicine, with evaluation and data diversity still the main bottlenecks.
Discussion (0). Continue with ORCID to comment.