arXiv AI

Foundation Agents Meet Agentic Deep Research: Evidence-Grounded Clinical Code Forecasting

arXiv:2608. 17075v1 Announce Type: cross Abstract: Next-encounter ICD forecasting predicts which standardized diagnosis codes will be documented at a future visit from the longitudinal record available beforehand.

arXiv AI

Aug 3

EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses

arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.

By Jiahui Li, Ruili Fang, Zishuai Liu, Yutong Guo, Nan Yang, Wenzhan Song, Jin Lu, Fei Dou

llms benchmarks

arXiv AI

1d ago

Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic

arXiv:2608. 16273v1 Announce Type: cross Abstract: Foresight-England (Foresight-E) is the first national-scale generative foundation model of electronic health records (EHRs), developed as a research pilot strictly for COVID-19 research.

By Simon Ellershaw, Christopher Tomlinson, Zeljko Kraljevic, Spiros Denaxas, Harry Hemingway, Cathie Sudlow, Angela M. Wood, Anoop D. Shah, Richard Dobson

llms benchmarks

arXiv AI

Jul 13

Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs

arXiv:2508. 14817v2 Announce Type: replace-cross Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).

By Skatje Myers, Dmitriy Dligach, Timothy A. Miller, Samantha Barr, James Landefeld, Yanjun Gao, Matthew Churpek, Anoop Mayampurath, Majid Afshar

llms rag

arXiv Machine Learning

Jul 20

LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models

arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.

By Jingteng Li, Alexander Capstick, Louise Rigny, Iona Biggart, Neil J Sebire, Payam Barnaghi

llms rag benchmarks

arXiv AI

Jul 28

DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs

arXiv:2607. 22555v1 Announce Type: new Abstract: Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with explanations.

By Mahmood Bayeshi, Veysel Kocaman, Muhammed Ali Naqvi, Yigit Gul, David Talby

llms rag agents benchmarks

arXiv AI

Jun 30

Primary ICD Category Prediction using LLM-based Probing

arXiv:2606. 28798v1 Announce Type: new Abstract: Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrate diagnostic signals from both clinical narratives and structured electronic health record (EHR) variables.

By Chengyuan Liu, Xinyue Zhang, Yao Li, Guanting Chen

llms rag multimodal

arXiv AI

Jul 13

LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making

arXiv:2607. 09322v1 Announce Type: new Abstract: In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making.

By Yanzhen Chen, Zihan Xu, Xiaocheng Zhang, Zhiting Fan, Weiqi Zhai, Hongxia Xu, Zuozhu Liu

llms rag agents benchmarks

arXiv AI

Jun 11

Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark

arXiv:2602. 19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide.

By Lalitha Pranathi Pulavarthy, Raajitha Muthyala, Aravind V Kuruvikkattil, Zhenan Yin, Rashmita Kudamala, Saptarshi Purkayastha

agents multimodal benchmarks safety

arXiv Machine Learning

Jun 8

GraphWalker: Patient Analogy Meets Information Gain for Clinical Reasoning with Large Language Models

arXiv:2604. 06684v2 Announce Type: replace Abstract: Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare.

By Yue Fang, Weibin Liao, Yuxin Guo, Jiaran Gao, Hongxin Ding, Jinyang Zhang, Xinke Jiang, Zhibang Yang, Junfeng Zhao, Yasha Wang, Liantao Ma

llms agents benchmarks

arXiv AI

Jun 8

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.

By Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra, Sandipan Sikdar, Koustav Rudra

llms benchmarks safety

arXiv Machine Learning

Jun 24

PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models

arXiv:2606. 24102v1 Announce Type: cross Abstract: Most electronic health record (EHR) foundation models encode clinical events as discrete event tokens from a fixed vocabulary and therefore cannot directly represent events containing unseen concepts or new combinations of concepts and attributes such as numeric values.

By Lin Lawrence Guo, Adam Paul Yan, Emily Vettese, Lillian Sung

arXiv AI

Jun 16

LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis

arXiv:2606. 16149v1 Announce Type: new Abstract: Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases.

By Minh-Ha Nguyen, Erica Gray, Chih-Ting Yang, Rizwan Hamid, Lingyao Li, Siyuan Ma, Thomas A. Cassini, Cathy Shyr

llms agents fine-tuning benchmarks

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