Peer-Reviewed Publication
Sci Rep2025;15(1):22474.July 2, 2025Journal Article
Quality assessment of large language models' output in maternal health.
Henrique A Lima1, Pedro H F S Trocoli-Couto1, Zorays Moazzam2, Leonardo C D Rocha3, Adriana Pagano4, Felipe F Martins5, Lucas T Brabo5, Zilma S N Reis1, Lisa Keder6, Aliya Begum7, Marcelo H Mamede1, Timothy M Pawlik8, Vivian Resende9
1Federal University of Minas Gerais Faculty of Medicine, Belo Horizonte, Brazil.
2Henry Ford Hospital, Detroit, MI, USA.
3Federal University of São João Del-Rei Computer Science Department, São João Del-Rei, Brazil.
4Federal University of Minas Gerais Arts Faculty, Belo Horizonte, Brazil.
5Asenion, Belo Horizonte, Brazil.
6Department of Gynaecology and Obstetrics, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
7Department of Gynaecology and Obstetrics, The Aga Khan University, Karachi, Pakistan.
8Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
9Federal University of Minas Gerais Faculty of Medicine, Belo Horizonte, Brazil. vivianresende.ufmg@gmail.com.
Abstract
Optimising healthcare is linked to broadening access to health literacy in Low- and Middle-Income Countries. The safe and responsible deployment of Large Language Models (LLMs) may provide accurate, reliable, and culturally relevant healthcare information. We aimed to assess the quality of outputs generated by LLMs addressing maternal health. We employed GPT-4, GPT-3.5, GPT-3.5 custom, Meditron-70…
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