TRACE is a deployable framework that enhances oncology language models by separating offline structure learning from online inference. It organizes oncology concepts and relations into an updatable tree‑relational structure, refines it with LM‑loss evidence, and retrieves compact prompt evidence during inference. The approach improves performance on ten classification tasks and a QA benchmark, outperforms vanilla RAG and generic GraphRAG, and provides interpretable evidence paths aligned with clinical reasoning.
By Jizheng Lai, Yingyun Li, Ying Qin, Haiyang Qian
arXiv:2608.28592v1 Announce Type: new
Abstract: Large language models (LLMs) achieve high scores on medical knowledge examinations, yet real-world oncology is not a knowledge test--it is a sequence o...
By Zhang Sheng, Jinming Li, Wangyang Chen, Zhiwei Bao, Yu YoSean Wang
arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.
By Jessica Sena, Shesadree Priyadarshani, Miguel Contreras, Bharat Gandhi, Scott Siegel, Subhash Nerella, Parisa Rashidi
The paper introduces ICD-Deepresearch, a workflow that combines foundation models for electronic health records (EHR) and language models with medical search and ICD dictionaries to forecast future ICD codes for upcoming clinical encounters. It evaluates candidate code transitions by linking patient evidence, external clinical relations, and exact code semantics within a fixed top‑K budget, using SparseEHR for initial priors, GPT‑5 for complementary forecasts, and a final selection step that validates, deduplicates, and ranks candidates. The method achieves patient‑averaged precision/recall of 24.60/35.09% on MIMIC‑III and 25.14/48.32% on MIMIC‑IV, with physicians rating 51–68% of its retrieved documents as useful, outperforming standalone GPT‑5 web search and Medical Deep Research.
By Junda Wang, Meysam Ghaffari, Akshat Choube, Mohsen Sharifi Renani, Hong Yu, Carlos Morato
arXiv:2609.22161v1 Announce Type: cross
Abstract: Medical large language models are commonly trained on mixtures of didactic data (e.g., textbooks) and clinical data (e.g., patient records), yet how...
By Yuzheng Fan, Haochun Wang, Sendong Zhao, Xiao Han, Ming Ma, Bing Qin
arXiv:2609.38543v1 Announce Type: new
Abstract: Constantly evolving real-world knowledge necessitates models to be updated continuously. Especially in medicine, as clinical evidence changes over time...
By Lukas Thede, Yash Kumar Atri, David Chen, Danielle Bitterman, Matthias Bethge, Tom Hartvigsen, Zeynep Akata
arXiv:2607. 05310v1 Announce Type: new Abstract: Model editing promises a fast, targeted way to correct post-deployment mistakes in medical vision-language models (VLMs) without costly retraining.
By Guli Zhu, Chenwei Wu, Liyue Shen
arXiv:2609.22281v1 Announce Type: new
Abstract: Foundation models have recently demonstrated strong capabilities across a wide range of medical imaging tasks. However, their performance in structured...
By Benjamin Renoust, Pierre Baudot, Tiffany Foriel, Yousra Haddou, Charles Voyton, Pierre-Henri Siot, Ezequiel Geremia, Danny Francis, Jean-Christophe Brisset, Val\'erie Bourd\`es, Sylvain Bodard, Benoit Huet
The paper introduces CAST, a concept-guided artifact suppression tuning framework that uses sparse autoencoders to identify and suppress note-specific artifacts in clinical language models. CAST labels latent features with an LLM-assisted pipeline and ICD‑10 constraints, then fine‑tunes the model while providing post‑hoc per‑concept attributions for auditability. In experiments on MIMIC‑IV discharge‑note mortality prediction, CAST outperforms standard fine‑tuned encoders and competes with strong LLM baselines while offering a feature‑level audit trail of clinical concepts and suppressed artifacts.
By Jin Mu, Guanhua Chen
CT‑ΔBench is a new benchmark designed to evaluate vision‑language models on longitudinal 3D medical imaging difference reporting. It provides patient‑level split data, change‑aware metrics, and physician‑validated references to assess clinically meaningful interval changes between two CT scans. The paper also introduces DeltaMed, a baseline model that directly reasons over paired CT scans, and compares it to an indirect two‑stage approach that first generates single‑timepoint reports before differencing.
By Kegeng Tang, Jingbo Wang, Shaogang Ren, Zihao Wang
arXiv:2610.14226v1 Announce Type: cross
Abstract: Purpose : To develop and evaluate CAPTOR, a deep-learning vision-language foundation model for oncologic chest-abdomen-pelvis (CAP) CT interpretation...
By S\'el\'ena Jean-Mactoux, Sophie Loizillon, Ghina Jardali, Amandine Cromb\'e, Nathalie Lassau, Alexandre B\^one
FD‑SCoPE is a language‑model framework that answers clinicians’ questions about systematic review evidence tables, exposing the underlying query, selected trials, and derivation rule for each answer. It handles both directly recorded attributes and derived attributes, achieving high accuracy on an oncology evidence table of 159 immune‑checkpoint inhibitor trials. After incorporating expert corrections, its performance on unseen questions improved from 77.9% to 84.9% F1.
By Manan Roy Choudhury, Suparno Roy Chowdhury, Swastik Sahoo, Muhammad Ali Khan, Kaneez Zahra Rubab Khakwani, Mohamad Bassam Sonbol, Irbaz Bin Riaz, Vivek Gupta