Evidence map›Paper›PMID 40375254›Full record

ArticleBMC public health2025

Evaluation of artificial intelligence (AI) chatbots for providing sexual health information: a consensus study using real-world clinical queries.

Phyu M Latt, Ei T Aung, Kay Htaik, Nyi N Soe, David Lee, Alicia J King, Ria Fortune, Jason J Ong, Eric P F Chow, Catriona S Bradshaw and 6 more

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

16 authors.

Phyu M LattMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia. phyu.latt@monash.edu.
Ei T Aung *Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Kay Htaik *Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Nyi N SoeMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
David LeeMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Alicia J KingSchool of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
Ria FortuneMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Jason J OngMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Eric P F ChowMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Catriona S BradshawMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Rashidur RahmanMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Matthew DeneenAlfred Health, Melbourne, Australia.
Sheranne DobinsonMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Claire RandallMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Lei ZhangMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Christopher K FairleyMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia. cfairley@mshc.org.au.

Funding

National Health and Medical Research Council GNT1172873National Health and Medical Research Council GNT1172900National Health and Medical Research Council GNT1193955
6 · The paper itself

Abstract

introductionArtificial Intelligence (AI) chatbots could potentially provide information on sensitive topics, including sexual health, to the public. However, their performance compared to nurses and across different AI chatbots, particularly in the field of sexual health, remains understudied. This study evaluated the performance of three AI chatbots - two prompt-tuned (Alice and Azure) and one standard chatbot (ChatGPT by OpenAI) - in providing sexual health information on questions that experienced sexual health nurses could correctly answer.

methodsWe analysed 195 anonymised sexual health questions received by the Melbourne Sexual Health Centre phone line. A panel of experts in a blinded order using a consensus-based approach evaluated responses to these questions from nurses and the three AI chatbots. Performance was assessed based on overall correctness and five specific measures: guidance, accuracy, safety, ease of access, and provision of necessary information. We conducted subgroup analyses for clinic-specific (e.g., opening hours) and general sexual health questions and a sensitivity analysis excluding questions that Azure could not answer.

resultsAlice demonstrated the highest overall correctness (85.2%; 95% confidence interval (CI), 82.1-88.0%), followed by Azure (69.3%; 95% CI, 65.3-73.0%) and ChatGPT (64.8%; 95% CI, 60.7-68.7%). Prompt-tuned chatbots outperformed the base ChatGPT across all measures. Among all outcome measures, all chatbots performed best on safety, with Azure achieving the highest safety score (97.9%; 95% CI, 96.4-98.9%), indicating the lowest risk of providing potentially harmful advice. In subgroup analysis, all chatbots performed better on general sexual health questions compared to clinic-specific queries. Sensitivity analysis showed a narrower performance gap between Alice and Azure when excluding questions Azure could not answer.

conclusionsPrompt-tuned AI chatbots demonstrated superior performance in providing sexual health information compared to base ChatGPT, with high safety scores particularly noteworthy. However, all AI chatbots showed susceptibility to generating incorrect information. These findings suggest the potential for AI chatbots as adjuncts to human healthcare providers for providing sexual health information while highlighting the need for continued refinement and human oversight. Future research should focus on larger-scale evaluations and real-world implementations.

Indexed as

Artificial IntelligenceSexual HealthAustraliaConsensusFemaleGenerative Artificial IntelligenceHumansMale

Identifiers

PMID40375254
PMCPMC12080182

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.