Aug 2026· Health Information Science and Systems· Vol 14· 0 citations· 21 references
Medicine
TL;DR
This work proposes MedRecord-CLIP, a knowledge-enhanced foundation model featuring a diagnosis-guided cross-attention mechanism to adaptively extract and fuse salient patient history with diagnostic representations that highlights the critical value of integrating personalized clinical context to enhance the generalizability and interpretability of fundus foundation models.
A FOundational LLM Trained on ThoughtMed-1M (FOLTMed), a scalable paradigm for advancing research on clinically grounded multimodal LLMs, achieved state-of-the-art performance across 42 medical VQA benchmark datasets, with a macro accuracy of 85.4%, and generated more clinically coherent responses on the ThoughtMed-1M...
Ling-Xuan Hou, Yu-Hua Xie, Yue Hu et al.· 0 citations
EndoVLM is presented, a novel vision-language FM pre-trained on over 348K endoscopic examinations, each pairing a clinical report with its corresponding image collection, which outperforms existing foundation models and remains competitive with task-specific methods.
Zhen-Yu Yi, Jian-Wei Xu, Yue Hu et al.· 0 citations
It is concluded that future progress depends on the collaborative optimization of datasets, preprocessing, and multimodal modeling, providing a roadmap toward robust clinical deployment, improved cross-domain generalization, and resource-efficient edge intelligence.
LocAnyMed-CoT-20K is derived, a rationale-augmented subset that connects anatomical context, visual observations, and spatial conclusions through structured reasoning and further improves cross-source generalization through fine-tuning.
Zi-Han Wang, Tong Liu, Zhi-Wei Wang et al.· 0 citations
A clinically curated Pan-Asia WSI--report dataset is introduced and the REG 2025 benchmark is established as a benchmark for evaluating WSI-based structured report generation and vision-language understanding in computational pathology, providing insights for the design of clinically grounded multimodal pathology model...
Yu-Mi Lee, Harim Oh, Hyo-yun Kim et al.· 0 citations
A unified framework for automatic report generation from SPECT bone scintigrams that integrates domain-adaptive representation learning, fine-grained image–text alignment, and anatomy-guided supervision is proposed, offering a valuable pathway to achieving trustworthy and intelligent diagnostic support within nuclear m...
Tao Song, Qiang Lin, Tong-Tong Li et al.· Applied intelligence (Boston...· 0 citations
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