Jul 2026· Journal of imaging informatics in medicine· 0 citations· 43 references
Medicine
TL;DR
Overall, while RAG shows promise for improving factual grounding in radiology AI, current evaluation paradigms likely overestimate real-world clinical readiness and future work should prioritize retrieval quality, clinically grounded evaluation, safety-critical error analysis, bias assessment, and deployment-relevant efficiency metrics to enable responsible clinical translation.
Automatic radiology report generation has gained increasing attention for its potential to assist in clinical reporting and reduce the workload of radiologists. Existing 3D radiology report generation methods employ multi-modal foundation model to encode volume-text inputs and produce diagnosis reports, while they igno...
Qiushi Yang, Wu-Yang Li, Xiaoqing Guo et al.· IEEE Transactions on Medical...· 0 citations
Overall, radiology-specific VFMs show promising transferability, but clinical translation remains constrained by limited data representativeness, heterogeneous benchmarks, incomplete reporting and insufficient deployment-oriented evaluation.
Alejandro Vergara-Richart, Xavier Rafael-Palou, A. Fuster-Matanzo et al.· arXiv.org· 0 citations
An evidence-guided retrieval-augmented framework EviRAG is proposed that decomposes retrieval into structured and unstructured alignment levels and substantially reduces clinical inconsistencies compared to strong medical vision-language baselines.
Yiyang Gu, Jiayue Fan, Kaili-May Liu et al.· Annual International ACM SIG...· 1 citation
Harrison.Rad 1.5 is the only system evaluated to meet the simulated FRCR passing standard and achieves the highest accuracy on closed-format clinical questions, across anatomical regions, on internal multi-body-part and mammography reporting, and on the primary clinically-aligned score for public chest reporting.
Suneeta Mall, V. Nekrasov, Ashnil Kumar et al.· 0 citations
Large language models can effectively improve the readability of radiology reports, yet all such models inherently suffer from output instability and information omission, so optimized structured prompts can substantially reduce the variability of model outputs and improve the accuracy of medical text translation.
Yun Mao, Chunyan Wang, Wei Wang et al.· Academic Radiology· 0 citations
The SPARC-Rad Benchmark is developed, a manually curated multimodal benchmark dataset and evaluation pipeline for assessing whether VLMs can provide reasoning for radiologic anatomy as a spatial system, supporting future model development, failure-mode analysis, and pre-deployment assessment.
Satvik Tripathi, M. E. Şeker, K. Quevada et al.· 0 citations
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