Domain Adaptation of a Large Language Model for Radiology Impression Generation – Multi-Reader Evaluation

A Comparative Study of Fine-Tuned Large Language Models for Turkish Radiology Report Generation

Benchmarking Large Language Model Performance in Generating and Assessing Radiology Objective Structured Clinical Examination

This study provides a benchmark of LLM performance for radiology OSCE-style content generation and evaluation during a specific snapshot of artificial intelligence development (August 2024).

Vision–language foundation models for multimodal medical image understanding and report generation: a narrative review with quantitative synthesis

Vision-language foundation models (VLFMs) for medical image understanding have advanced rapidly, yet evidence supporting clinical deployment remains fragmented. This narrative review synthesizes architectures, training paradigms, benchmarks, public datasets, and translation barriers. Drawing on more than 50 original st...

A visual large language foundational model for medical image recognition using clinician-contributed online resources

Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine. However, their application in medical settings remains limited by the scarcity of visual question answering (VQA) datasets that capture clinical reasoning and explicit image-text alignm...

BioEVAL: A global, multi-institutional benchmark of large language and multimodal models for bioengineering

Diverse cloud-scale foundation/multimodal models and locally deployable models suitable for inference on consumer-grade GPUs are evaluated, achieving the highest accuracy of up to 90% on MCQs, similarity score of 0.72 on literature synthesis, and accuracy of 80% on a small sample of multimodal reasoning questions, with...

Representation-guided in-context learning for medical image interpretation with multimodal large language models

RG-ICL is introduced, a training-free inference framework that retrieves query-aligned demonstrations using frozen encoders, without task-specific parameter updates, and indicates that for medical image interpretation, curating which reference cases an MLLM sees is a practical alternative to retraining it.