The article proposes a framework called quantitative evidence mining to transform biomedical findings into structured, context-rich evidence units. It outlines core elements such as claim, measured entity, value, comparator, population, conditions, temporal context, uncertainty, provenance, validation, and expert review. The authors present an eight-stage reference architecture and emphasize that plausibility should remain multidimensional rather than collapsed into a single truth label, linking extraction to evidence synthesis for applications like clinical trials, biomarker research, and knowledge-graph construction.
By Negin Sadat Babaiha, Stefan Geissler, Marie-Christine Simon, Martin Hofmann-Apitius, Marc Jacobs
The paper presents an autonomous AI agent that combines ReAct and Retrieval-Augmented Generation to classify the severity of genetic diseases using 10,211 Human Phenotype Ontology terms. It applies ACMG severity guidelines and ACOG quality-of-life criteria, retrieving PubMed literature to produce interpretable reasoning chains and verify claims. The agent achieved 93.55% accuracy on phenotype-level classification and identified 3,283 autosomal recessive gene pairs with severe presentations, with 95.2% concordance in external validation.
By Tohid Ghasemnejad, Ahmadreza Argha, Mark Grosser, John Wang, Min Yang, Thantrira Porntaveetus, Tony Roscioli, Nigel H. Lovell, Mahmoud Aarabi, Hamid Alinejad-Rokny
arXiv:2608.30393v1 Announce Type: new
Abstract: Biomedical artificial intelligence (AI) systems increasingly extract, organize, and reuse scientific claims from literature, clinical trials, and regul...
By Negin Sadat Babaiha, Stefan Geissler, Marie-Christine Simon, Martin Hofmann-Apitius, Marc Jacobs
EvidenceNet is a disease‑specific dataset that transforms full‑text biomedical literature into structured evidence records and graph representations, preserving study design, provenance, and quantitative support. Using an LLM‑assisted pipeline, it extracts experimentally grounded findings, normalizes entities, scores evidence quality, and links related records via typed semantic relations. The released subsets—EvidenceNet‑HCC and EvidenceNet‑CRC—contain thousands of evidence records and richly connected graphs, with high extraction and relation‑type accuracy, enabling retrieval‑augmented question answering and graph‑based tasks such as link prediction and target prioritization.
By Chang Zong, Jinyu Chen, Sicheng Lv, Si-tu Xue, Huilin Zheng, Jian Wan, Lei Zhang
arXiv:2610.16175v1 Announce Type: new
Abstract: Real-world studies (RWS) analyze routinely collected healthcare data, such as electronic health records and insurance claims, to estimate treatment eff...
By Zhaoyu Fan, Bowen Han, Jincheng Ou, Yanwei Ren, Zehua He, Kaiyu Huang, Pengcheng He, Shanshan Ni, Chengchen Gong, Shuai Xue, Qingjiang Shi
The paper introduces ClinGeX, a large‑language‑model agent that uses 105 curated executable skills to automate clinical genomic analysis. In benchmark tests, ClinGeX achieved 71.7% macro‑averaged task‑level concordance, outperforming the best comparator at 45.7%. Human‑AI collaboration studies showed ClinGeX significantly improved case‑level concordance across variant analysis, rare disease interpretation, and oncology decision‑making, while also reducing interpretation time.
By Qiaoyu Zheng, Weike Zhao, Yanjie Fan, Ye Cao, Mengqi Jiang, Xiao Zhou, Xiaoman Zhang, Pengcheng Qiu, Yongguo Yu, Yanfeng Wang, Ya Zhang, Chaoyi Wu, Weidi Xie
arXiv:2603. 05308v3 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification.
By Qiao Jin, Yin Fang, Lauren He, Yifan Yang, Guangzhi Xiong, Zhizheng Wang, Nicholas Wan, Joey Chan, Donald C. Comeau, Robert Leaman, Charalampos S. Floudas, Aidong Zhang, Michael F. Chiang, Yifan Peng, Zhiyong Lu
OpenMTB‑Audit is an open‑source benchmark that tests large language models on 500 synthetic non‑small cell lung cancer cases, covering five adversarial error categories and four safety labels: Supported, Partially Supported, Unsupported, and Insufficient Information. The study found that all eight tested LLMs over‑refused Partially Supported recommendations, collapsing labels to achieve high safety scores. A deterministic seven‑module framework, MTB‑AuditAgent, was introduced to reduce over‑refusal to 6.7% and reach 91.2% accuracy, while an oncologist annotation study highlighted disagreement around the boundary between information sufficiency and treatment optimization.
By Negin Ashrafi, Jia Luo, Stacey M. Frumm, Roxana Daneshjou
arXiv:2608.30912v1 Announce Type: new
Abstract: Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge rele...
By Bahar \.Ilgen, Yiannos Tolias, Denise K\"uhnert, Paraskevi Papadopoulou, Magnus Westerlund, Dominik Heider, Katharina Ladewig, Georges Hattab
The paper introduces BAR, a Budget‑Aware LLM Reasoning framework that enhances post‑discharge risk prediction by integrating external medical knowledge graphs (KGs). BAR refines KGs into disease‑specific evidence graphs with support scores and provenance, then uses an LLM to plan, navigate, and verify evidence within a patient‑specific budget. Experiments on MIMIC‑III and MIMIC‑IV across eight diseases show BAR improves AUPRC by 3.4 points, raises citation precision from 59.8% to 77.9%, and uses only 62‑65% of the allotted budget.
By Chen Chen, Dongjie Wang, Mei Liu, Zijun Yao
arXiv:2606. 11208v1 Announce Type: cross Abstract: Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions.
By Elias Hossain, Sanjeda Sara Jennifer, Sabera Akter Bushra, Niloofar Yousefi
MedRAGChecker is a claim-level verification framework designed for biomedical retrieval‑augmented generation (RAG). It decomposes generated answers into atomic claims and assesses each claim’s support by combining evidence‑grounded natural language inference with biomedical knowledge‑graph consistency signals. The aggregated claim decisions provide diagnostics that distinguish retrieval and generation failures, such as faithfulness, under‑evidence, contradiction, and safety‑critical errors, and the system is distilled into compact models for scalable evaluation.
By Yuelyu Ji, Min Gu Kwak, Hang Zhang, Xizhi Wu, Chenyu Li, Yanshan Wang