Toward reviewable medical evidence synthesis for care delivery

Abstract Large language model systems can retrieve and cite medical evidence, yet their outputs may remain difficult to review during care. We define reviewability through evidence adequacy for action-changing claims, exact source verification, and presentation matched to a specified reviewer and decision point. We propose a workflow-adaptive framework with a stable audit core, claim-specific stopping and escalation, and technical and human-factors evaluation.

Authors

Institutions

Publication Details

Journal
npj Digital Medicine
Published
2026-09-19
DOI
https://doi.org/10.1038/s41746-026-03195-z
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Toward reviewable medical evidence synthesis for care delivery

Zonghai Yao, Hong Yu
npj Digital Medicine
Machine Learning in Healthcare
article

Toward reviewable medical evidence synthesis for care delivery

Zonghai Yao, Hong Yu
article en

Abstract

Abstract Large language model systems can retrieve and cite medical evidence, yet their outputs may remain difficult to review during care. We define reviewability through evidence adequacy for action-changing claims, exact source verification, and presentation matched to a specified reviewer and decision point. We propose a workflow-adaptive framework with a stable audit core, claim-specific stopping and escalation, and technical and human-factors evaluation.

npj Digital Medicine
University of Massachusetts Lowell (US), University of Massachusetts Amherst (US), Bedford VA Research Corporation (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Machine Learning in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.