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Clinical Accuracy of Large Language Models and Google Search Responses to Postpartum Depression Questions: A Cross-Sectional Study
Emre Sezgin;
Faraaz Chekeni;
Jennifer Lee;
Sarah Keim
ABSTRACT
Postpartum depression (PPD) affects about 1 in 8 women in the months after delivery. As Large Language Model (LLM)-supported applications are becoming an integral part of online information seeking behavior, it is necessary to assess the capability and validity of these applications in addressing prevalent mental health conditions. In this study, we assessed the quality of responses from ChatGPT, Bard and Google Search to frequently asked PPD questions based on clinical accuracy. Results showed ChatGPT differed in the quality of responses against others, and it demonstrated generally higher quality (more clinically accurate) responses compared to Bard and Google Search.
Citation
Please cite as:
Sezgin E, Chekeni F, Lee J, Keim S
Clinical Accuracy of Large Language Models and Google Search Responses to Postpartum Depression Questions: Cross-Sectional Study