Evidence map›Paper›PMID 41758155›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2026

MedFactEval and MedAgentBrief: A Framework and Workflow for Generating and Evaluating Factual Clinical Summaries.

Françcois Grolleau, Emily Alsentzer, Timothy Keyes, Philip Chung, Akshay Swaminathan, Asad Aali, Jason Hom, Tridu Huynh, Thomas Lew, April Liang and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Françcois GrolleauCenter for Biomedical Informatics Research, Stanford University, Stanford, CA, USA, grolleau@stanford.edu.
Emily AlsentzerDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Timothy KeyesStanford Health Care, Palo Alto, CA, USA.
Philip ChungDepartment of Anesthesiology and Pain Medicine, Stanford Medicine, Stanford, CA, USA.
Akshay SwaminathanDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Asad AaliDepartment of Radiology, Stanford University, Stanford, CA, USA.
Jason HomDepartment of Medicine, Stanford University, Stanford, CA, USA.
Tridu HuynhDepartment of Medicine, Stanford University, Stanford, CA, USA.
Thomas LewDepartment of Medicine, Stanford University, Stanford, CA, USA.
April LiangDepartment of Medicine, Stanford University, Stanford, CA, USA.
Weihan ChuDepartment of Medicine, Stanford University, Stanford, CA, USA.
Natasha SteeleDepartment of Medicine, Stanford University, Stanford, CA, USA.
Christina LinDepartment of Medicine, Stanford University, Stanford, CA, USA.
Jingkun YangDepartment of Medicine, Stanford University, Stanford, CA, USA.
Kameron BlackDepartment of Medicine, Stanford University, Stanford, CA, USA.
Stephen MaDepartment of Medicine, Stanford University, Stanford, CA, USA.
Fateme N HaredashtCenter for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Nigam H ShahCenter for Biomedical Informatics Research, Stanford University, Stanford, CA, USA2Department of Biomedical Data Science, Stanford University, Stanford, CA, USA7Department of Medicine, Stanford University, Stanford, CA, USA.
Kevin SchulmanStanford Clinical Excellence Research Center, Stanford University, Stanford, CA, USA7Department of Medicine, Stanford University, Stanford, CA, USA.
Jonathan H ChenCenter for Biomedical Informatics Research, Stanford University, Stanford, CA, USA6Stanford Clinical Excellence Research Center, Stanford University, Stanford, CA, USA7Department of Medicine, Stanford University, Stanford, CA, USA.

Funding

Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant BacteriaR01AI178121 · NIAID · STANFORD UNIVERSITY · PI JONATHAN H. CHEN · 2023 to 2026
$3.1M
NIAID NIH HHS R01 AI178121
6 · The paper itself

Abstract

Evaluating factual accuracy in Large Language Model (LLM)-generated clinical text is a critical barrier to adoption, as expert review is unscalable for the continuous quality assurance these systems require. We address this challenge with two complementary contributions. First, we introduce MedFactEval, a framework for scalable, fact-grounded evaluation where clinicians define high-salience key facts and an "LLM Jury"-a multi-LLM majority vote-assesses their inclusion in generated summaries. Second, we present MedAgentBrief, a model-agnostic, multi-step workflow designed to generate high-quality, factual discharge summaries. To validate our evaluation framework, we established a gold-standard reference using a seven-physician majority vote on clinician-defined key facts from inpatient cases. The MedFactEval LLM Jury achieved almost perfect agreement with this panel (Cohen's κ = 81%), a performance statistically non-inferior to that of a single human expert (κ = 67%, P < 0.001). Our work provides both a robust evaluation framework (MedFactEval) and a high-performing generation workflow (MedAgentBrief), offering a comprehensive approach to advance the responsible deployment of generative AI in clinical workflows.

Indexed as

WorkflowComputational BiologyElectronic Health RecordsHumansLarge Language Models

Identifiers

PMID41758155
PMCPMC13182771

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.