arXiv AI By Md. Najib Hasan (Wichita State University, USA), Imran Ahmad (Wichita State University, USA), Sourav Basak Shuvo (Khulna University of Engineering and Technology, Bangladesh), Md. Mahadi Hasan Ankon (Khulna University of Engineering and Technology, Bangladesh), Nazmul Siddique (Ulster University, UK), Hui Wang (Queen's University Belfast, UK)

DL$^3$M: A Vision-to-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Models

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arXiv AI
Oct 19

Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI

The paper introduces a model‑agnostic framework that lets frozen large language models (LLMs) and vision‑language models (VLMs) learn from deployment experience without retraining. It does so by adding three external expertise components—a Skill for guiding reasoning and tool use, a Knowledge Memory for storing reliable facts, and a Multimodal Knowledge Base for visual examples—and by using a validation strategy that updates only when new cases improve performance without harming past performance. Experiments on six benchmarks with both open‑weight and closed‑source models show up to a 34.2% performance gain on medical tasks, generalization to unseen cases, and transferability to other models and non‑medical domains.

By Yexiao He, Yucheng Tang, Pengfei Guo, Yufan He, Andriy Myronenko, Can Zhao, Ang Li, Daguang Xu, Dong Yang
arXiv AI
Sep 17

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

The paper introduces ThoughtMed-1M, a large-scale medical visual question answering dataset built from de‑identified images and clinician‑generated commentaries, designed to capture structured clinical reasoning and image‑text alignment. Using this dataset, the authors train FOLTMed, a foundational large language model that achieves state‑of‑the‑art performance on 42 medical VQA benchmarks, with a macro accuracy of 85.4% and improved factuality and similarity metrics over existing models.

By Lingxuan Hou, Yuhua Xie, Yue Hu, Yan Zhuang, Junqi Li, Chengzhi Xia, Binh Phu Nguyen, Abubakar Siddique, Minh Nguyen, Yao Hou, Yanju Bao, Kexin Liu, Ke Chen, Jianjun Sun, Zeqi Li, Trung Nguyen, Jiangli Lin
arXiv AI
Sep 10

A visual large language foundational model for medical image recognition using clinician-oriented social media

arXiv:2609.06914v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine. However, their a...

By Lingxuan Hou, Yuhua Xie, Yue Hu, Yan Zhuang, Junqi Li, Chengzhi Xia, Binh Phu Nguyen, Abubakar Siddique, Minh Nguyen, Yao Hou, Yanju Bao, Kexin Liu, Ke Chen, Jianjun Sun, Zeqi Li, Trung Nguyen, Jiangli Lin
arXiv AI
Sep 21

LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering

LiteMedCoT-VL is a parameter‑efficient pipeline that transfers chain‑of‑thought reasoning from a 235B teacher model to a 2B student model using LoRA fine‑tuning on explanation‑enriched data. The approach enables a compact vision‑language model to perform medical visual question answering without relying on image captions, achieving 64.9% accuracy on the PMC‑VQA benchmark—an 11‑point improvement over the zero‑shot Qwen3‑VL‑4B baseline. Visual grounding analysis confirms that the model bases its predictions on image content rather than textual priors.

By Runze Ma, Shunbo Jia, Haonan Lyu, Guo Liu, Caizhi Liao