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Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial…

By PulseAugur Editorial · [5 sources] ·

Researchers have developed new benchmarks and frameworks to evaluate and improve the performance of large language models (LLMs) in clinical settings. PhysicianBench offers a comprehensive evaluation for LLM agents on real-world electronic health record (EHR) tasks, revealing current limitations with success rates below 50%. Additionally, ReMedi provides a framework to enhance clinical outcome prediction from EHRs by generating improved rationale-answer pairs for fine-tuning. Another approach introduces a lightweight retrieval-augmented generation method for scalable patient-trial matching, achieving comparable performance to end-to-end LLM methods with reduced computational cost. AI

IMPACT These advancements aim to improve the accuracy and efficiency of LLMs in healthcare, potentially leading to better patient care and trial matching.

RANK_REASON Multiple research papers introduce new benchmarks and frameworks for evaluating and improving LLM performance in clinical settings.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial…

COVERAGE [5]

  1. arXiv cs.CL TIER_1 English(EN) · Zhan Qu, Michael F\"arber ·

    MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs

    arXiv:2512.20822v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly applied to medicine, yet their adoption is limited by concerns over reliability and safety. Existing evaluations either test factual medical knowledge in isolation or assess patient-…

  2. arXiv cs.AI TIER_1 English(EN) · Ruoqi Liu, Imran Q. Mohiuddin, Austin J. Schoeffler, Kavita Renduchintala, Ashwin Nayak, Prasantha L. Vemu, Shivam C. Vedak, Kameron C. Black, John L. Havlik, Isaac Ogunmola, Stephen P. Ma, Roopa Dhatt, Jonathan H. Chen ·

    PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments

    arXiv:2605.02240v1 Announce Type: new Abstract: We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record (EHR) environments. Existing medical agent benchmarks primarily focus on static …

  3. arXiv cs.CL TIER_1 English(EN) · Yushi Cao, Yiming Chen, Hongchao Jiang, Hung-yi Lee, Robby T. Tan ·

    ReMedi: Reasoner for Medical Clinical Prediction

    arXiv:2605.01474v1 Announce Type: new Abstract: Predicting future clinical outcomes from electronic health records (EHR) remains challenging due to the complexity and heterogeneity of patient data. LLMs have shown strong potential for such predictive tasks, yet existing approache…

  4. arXiv cs.CL TIER_1 English(EN) · Xiaodi Li, Yang Xiao, Munhwan Lee, Konstantinos Leventakos, Young J. Juhn, David Jones, Terence T. Sio, Wei Liu, Maria Vassilaki, Nansu Zong ·

    Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial Matching

    arXiv:2604.22061v1 Announce Type: new Abstract: Patient-trial matching requires reasoning over long, heterogeneous electronic health records (EHRs) and complex eligibility criteria, posing significant challenges for scalability, generalization, and computational efficiency. Exist…

  5. arXiv cs.CL TIER_1 English(EN) · Nansu Zong ·

    Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial Matching

    Patient-trial matching requires reasoning over long, heterogeneous electronic health records (EHRs) and complex eligibility criteria, posing significant challenges for scalability, generalization, and computational efficiency. Existing approaches either rely on full-document proc…

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