Evidence map›Paper›PMID 42656376›Full record

ArticleFrontiers in psychiatry2026

Evaluating the accuracy, reliability, and readability of AI chatbots in delivering postpartum depression information.

Man Yang, Hui Liu, Shuyan Lin, Linyan Liu, Yunxia Wang, Xiuling Qiu, Ling Wei, Li Zhou, Xinxin Wang

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Man Yang *Department of Obstetrics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China.
Hui Liu *Department of Obstetrics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China.
Shuyan Lin *Department of Obstetrics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China.
Linyan LiuDepartment of Obstetrics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China.
Yunxia WangDepartment of Obstetrics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China.
Xiuling QiuDepartment of Obstetrics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China.
Ling WeiDepartment of Obstetrics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China.
Li ZhouDepartment of Obstetrics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China.
Xinxin WangDepartment of Obstetrics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Postpartum depression (PPD) is a common perinatal psychiatric disorder with significant implications for maternal and infant health. Artificial intelligence (AI) chatbots have emerged as widely accessible tools for health information, but their performance in providing accurate, reliable, and readable PPD related information remains underexplored. Methods: We evaluated six AI chatbots, ChatGPT-5, ChatGPT-4o, Claude Sonnet 4.5, DeepSeek-V3.2, DeepSeek-R1, and Gemini 2.5 Pro, using 200 standardized multiple choice questions (MCQs) on PPD to assess validity. ChatGPT-4o and DeepSeek-R1 were included only in the MCQ based validity analysis as earlier version comparators. Reliability and readability were further assessed using 20 core public education questions in the four latest models: ChatGPT-5, Claude Sonnet 4.5, DeepSeek-V3.2, and Gemini 2.5 Pro. Chatbot performance was assessed across three dimensions: validity, reliability, and readability. Each MCQ was presented three times independently, and each core public education question was assessed once per model. Results: In the six model MCQ based validity analysis, ChatGPT-5 achieved the highest overall accuracy on MCQs (97.50% ± 0.50%). In the four models reliability and readability analyses, ChatGPT-5 obtained the highest DISCERN, EQIP, and GQS scores, suggesting relatively better content quality and user oriented usefulness. However, JAMA benchmark scores were low across all models, including ChatGPT-5, indicating limited transparency, source attribution, currency, and disclosure. All models produced outputs exceeding the recommended sixth grade reading level, although ChatGPT-5 and Gemini 2.5 Pro were relatively more accessible. Conclusion: AI chatbots, particularly ChatGPT-5, showed potential as supplementary tools for providing postpartum depression related information, especially in standardized MCQ based assessment. However, this study did not evaluate clinical safety, patient comprehension, user behavior, or real world effectiveness, and suboptimal readability may limit accessibility for users with lower health or digital literacy. Inadequate transparency, limited source attribution, and suboptimal readability indicate that AI chatbots should not be used as autonomous sources of postpartum mental health guidance and should not replace professional assessment or care.

Indexed as

artificial intelligencehealth informationlarge language modelspatient educationpostpartum depression

Identifiers

PMID42656376
PMCPMC13506791

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.