Efficacy of a Large Language Model Data Extraction System in Evidence Reviews for Emerging Infectious Diseases: A Randomized Crossover Trial.

Authors

Masahiro Ishikane, Yuki Kataoka, Yasushi Tsujimoto, Yuki Moriyama, Yukimasa Matsuzawa, Norio Ohmagari

Published in

Open forum infectious diseases. Volume 13. Issue 7. Pages ofag401. Epub Jul 23, 2026.

Abstract

Rapid evidence synthesis during emerging infectious and re-emerging disease outbreaks is critical, yet traditional systematic reviews rarely meet urgent timelines. Large language models (LLMs) may accelerate evidence synthesis by extracting data from publications. We compared an LLM-assisted data extraction system with manual extraction.
We conducted a 1:1, open-label, 2-period, randomized crossover trial at the National Center for Global Health and Medicine, a national reference center for emerging infectious diseases in Japan (2025). Five experienced reviewers extracted predefined items from mpox-related articles under 2 conditions: (i) LLM-assisted extraction using OpenAI's o3 model to generate structured summaries and (ii) manual review of PDF files. The primary outcome was task completion time; secondary outcomes were extraction accuracy and adverse events. Mixed-effects models included condition as a fixed effect and participant and paper IDs as random effects. The protocol, source code, and data are available at https://github.com/SRWS-PSG/emerging_infection_24K13518_open.
Five evaluators (4 physicians and 1 pharmacist; 6-10 years postgraduation) completed 20 task-level evaluations (LLM, n = 9; no LLM, n = 11). Mean completion time was 27.5 minutes with LLM assistance versus 34.5 minutes without. The LLM-assisted condition was 7.9 minutes faster on average (95% CI -1.5 to 17.3; P = .099). Extraction accuracy was 100% in both conditions, and no adverse events were reported.
LLM assistance might reduce data extraction time by ∼23% (7.9 minutes per article; 95% CI -1.5 to 17.3 minutes) with no observed loss of accuracy. Although statistical uncertainty remains, LLM integration may offer practical value for rapid evidence synthesis during public health emergencies as tools and prompting strategies mature.

PMID:
42494833
Bibliographic data and abstract were imported from PubMed on 24 Jul 2026.

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