The paper introduces RetinaGroundBench, a new benchmark for visual grounding of retinal OCT images, featuring 815 B-scans annotated with 4,537 bounding boxes for 13 biomarkers across 8 diseases. It evaluates 27 multimodal large language models (MLLMs) and finds that visual grounding is largely unsolved, with the best model achieving only a macro F1 of 23.7%. The authors also present RetGrounder, an 8‑B parameter MLLM fine‑tuned for OCT, which raises grounding F1 to 33.9%, a 43% relative improvement.
Multimodal large language models (MLLMs) can answer clinical questions about medical images, but trustworthy use requires visual grounding: models should localize the pathology they report rather than...
arXiv:2609.32352v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging r...
arXiv:2607. 03959v1 Announce Type: cross Abstract: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, highlighting the need for accurate and accessible screening tools.
arXiv:2606. 06696v1 Announce Type: cross Abstract: Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling cellular features in microscopy.
Co-Annotator is a clinical AI system that distills expert gaze and dictation into two guidance components: a gaze‑aligned Vision Transformer that highlights fixation‑aligned areas of interest (AOIs) and an ontology‑bounded vision‑language model that pre‑fills editable biomarker summaries for retinal OCT. In controlled studies, each modality independently improved diagnostic accuracy and biomarker generation, and when combined across two academic institutions, the system increased correct diagnoses per minute by 40% and reduced comment editing time by 67% without compromising accuracy.
The paper introduces MedREAL, a unified framework that aligns linguistic reasoning with spatial grounding for medical visual question answering and segmentation. MedREAL employs Seg Anchored Reasoning Pooling (SARP) to extract semantic evidence from segmentation tokens and a Reasoning-to-Visual (R2V) fusion mechanism to integrate these features into a segmentation pipeline. Using the newly created MedRAVS-13K dataset, MedREAL achieves superior performance, reporting 68.49% gIoU and 70.47% cIoU, and generates evidence masks that consistently match textual diagnoses.
RetiJev is a family of 0.8B‑9B decision models designed for ophthalmic clinical decision support, offering fast, accurate, and privacy‑preserving inference without autoregressive decoding. The authors created RetiJev‑Data, a source‑grounded corpus of 78,158 clinical‑decision and diagnosis questions, 50,074 guideline‑grading questions, and a 24,662‑question evaluation benchmark. RetiJev outperforms a 27B LLM and the commercial Jev 1.13 model on guideline grading and clinical decisions, achieving the highest accuracy while answering a case in 158 ms on a single GPU—3.5× faster than LLM generation and 470× faster than reasoning.
arXiv:2609.00866v1 Announce Type: cross Abstract: The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pa...
The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pathology report generation remains limited by the sc...
arXiv:2606. 15129v1 Announce Type: cross Abstract: Color fundus photography (CFP) is the mainstay for large-scale retinal screening, yet its diagnostic capacity is constrained by the lack of depth-resolved structural information.
Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical overlap or aggregate clinical correctness. However, such metrics do not test whether individual diagnostic statements stem from the actual pathological evidence visible in the image.
arXiv:2601.06847v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) can generate convincing clinical narratives, yet frequently struggle to visually ground their statements. We po...