Quality assessment of large language models' output in maternal health.
Journal:
Scientific reports
Published Date:
Jul 2, 2025
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-70b. Using mixed-methods, cross-sectional survey approach, specialists from Brazil, United States, and Pakistan assessed LLM-generated responses in their native languages to a set of three questions relating to maternal health. Evaluators assessed the answers in technical and non-technical scenarios. The LLMs' responses were evaluated regarding information quality, clarity, readability and adequacy. Of the 47 respondents, 85% were female, mean age of 50 years old, with a mean of 19 years of experience (volume of 110 assisted pregnancies monthly). Scores attributed to answers by GPT-3.5 and GPT-4 were consistently higher [Overall, GPT-3.5, 3.9 (3.8-4.1); GPT-4.0, 3.9 (3.8-4.1); Custom GPT-3.5, 2.7 (2.5-2.8); Meditron-70b, 3.5 (3.3-3.6); p = 0.000]. The responses garnered high scores for clarity (Q&A-1 3.5, Q&A-2 3.7, Q&A-3 3.8) and for quality of content (Q&A-1 3.2, Q&A-2 3.2, Q&A-3 3.7); however, they differed by language. The commonest limitation to quality was incomplete content. Readability analysis indicated that responses may require high educational level for comprehension. Gender bias was detected, as models referred to healthcare professionals as males. Overall, GPT-4 and GPT-3.5 outperformed all other models. These findings highlight the potential of artificial intelligence in improving access to high-quality maternal health information. Given the complex process of generating high-quality non-English databases, it is desirable to incorporate more accurate translation tools and resourceful architectures for contextualization and customisation.
