arXiv Machine Learning

Semantic Reasoning in Medicine: The Role of Knowledge Graphs Across Five Key Domains

arXiv:2606. 15155v1 Announce Type: new Abstract: Knowledge graphs (KGs) have emerged as a promising solution for integrating and reasoning over complex biomedical and clinical data in healthcare.

arXiv AI

Jul 10

Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning

arXiv:2607. 07761v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as important tools in healthcare, showing growing potential for clinical reasoning and patient care.

By Qi Peng, Jiatong Li, Sirui Huang, Yiyang Jiang, Kaisong Gong, Ronger Ding, Shijie Ye, Changmeng Zheng, Yi Cai, Xiaobo Yang, Jin Huang, Xiao-Yong Wei, Qing Li

llms benchmarks safety

arXiv AI

Jun 11

Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning

arXiv:2606. 11675v1 Announce Type: new Abstract: Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap.

By Haoyang Zeng, Yuanxi Fu, Rongzhen Li, Yuming Yang, Xiao Sun, Jingwang Huang, Gujie Shao, Guohui Xiang, Quan Lu, Dongfan Ye, Xuetao Chen, Jiang Zhong, Kaiwen Wei, Zhi Xu

llms nlp reinforcement-learning benchmarks

arXiv Machine Learning

Jun 5

HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care

arXiv:2606. 05994v1 Announce Type: new Abstract: Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain.

By Thummaluru Siddartha Reddy, Vempalli Naga Sai Saketh, Yash Punjabi, Mahesh Chandran

benchmarks

arXiv AI

Jun 3

ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning

arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).

By Bo-Hong Wang, Baicheng Peng, Ruilin Wang, Jun Bai, Ziyang Song, Yue Li

llms multimodal benchmarks safety

arXiv AI

Jun 19

MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization

arXiv:2606. 20164v1 Announce Type: cross Abstract: Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions.

By Aueaphum Aueawatthanaphisut

llms rag agents nlp multimodal

arXiv AI

Aug 11

H2: A Dual Hybrid Semantic Data Lake Architecture for Medical Data Harmonization with Human-In-the-Loop verified, LLM Driven Metadata Annotation System

arXiv:2608. 08056v1 Announce Type: new Abstract: Medical data, by its nature, exhibit a high degree of heterogeneity on multiple levels ranging from (a) different modalities like images, text and time series, (b) diverse tabular schemata introduced by institutions and (c) completely unstructured textual information data provided by healthcare professionals.

By Ioannis N. Tzortzis, Georgia Kapetadimitri, Agapi Davradou, Nefeli Kousta, Nikolaos Bakalos, Ioannis Rallis, Dimitrios Kalogeras, Nikolaos Doulamis, Anastasios Doulamis

llms data-engineering

arXiv AI

Jun 30

NEURON: A Neuro-symbolic System for Grounded Clinical Explainability

arXiv:2605. 01189v2 Announce Type: replace Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability.

By Anuradha Chandrasekaran, Dimitrios Zikos, Mutlu Mete, Alan Pang, Brady D. Lund, Kewei Sha

llms rag safety

arXiv Machine Learning

Jul 17

NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis

arXiv:2607. 14314v1 Announce Type: new Abstract: Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling.

By Lincan Li, Zheng Chen, Yushun Dong

llms rag safety

arXiv Machine Learning

Aug 5

PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks

arXiv:2608. 02692v1 Announce Type: new Abstract: Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing single modalities or data sources.

By Julia Gehrmann, Lars Quakulinski, Hamza Naseem, Oya Beyan

multimodal benchmarks

arXiv AI

Jul 2

RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation

arXiv:2607. 00147v1 Announce Type: new Abstract: Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space.

By Deyang Jiang, Haoran Wu, Ziyi Wang, Yiming Rong, Yunlong Zhao, Ye Jin, Bo Xu

llms rag agents reinforcement-learning benchmarks

arXiv AI

Jul 20

GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis

arXiv:2607. 15280v1 Announce Type: new Abstract: Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering.

By Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan

llms agents efficiency

arXiv AI

Jun 9

A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology

arXiv:2606. 08093v1 Announce Type: new Abstract: Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices.

By Zhe Xu, Zhengyu Zhang, Zhiyuan Cai, Jiahao Xu, Yijie Lin, Ziyi Liu, Junlin Hou, Hongyi Wang, Yuxiang Nie, Ling Liang, Yihui Wang, Yingxue Xu, Ronald Cheong Kin Chan, Li Liang, Hao Chen

agents multimodal benchmarks

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