Evidence map›Paper›PMID 41250005›Full record

ArticleBMC public health2025

AI chatbots as 'pocket doctors': intimate health support for young women in Lebanon.

Anthony Mina, Elie Ghadban, Tigresse Boutros, Naya Saade, Lynn Fayad, Michel Abi Karam, Sabine Breidi, Marc Elias, Maryline Ghosh, Layane El Khoury and 2 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

12 authors.

Anthony Mina *School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon. anthony.n.mina@net.usek.edu.lb.
Elie Ghadban *School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Tigresse BoutrosSchool of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Naya SaadeSchool of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Lynn FayadSchool of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Michel Abi KaramSchool of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Sabine BreidiSchool of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Marc EliasSchool of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Maryline GhoshSchool of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Layane El KhouryFaculty of Medicine, American University of Beirut Medical Center, Beirut, Lebanon.
Nicolas NaassanFaculty of Medicine, American University of Beirut Medical Center, Beirut, Lebanon.
Raghid El KhourySchool of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn conservative societies such as Lebanon and the broader Middle East and North Africa region, gynecological and intimate health issues are heavily stigmatized, limiting young women's access to care due to fear of judgment, privacy concerns, and cultural taboos. These barriers often result in delayed diagnoses and poorer health outcomes. Large Language Models, such as ChatGPT and Gemini, have emerged as digital tools offering anonymity, reduced embarrassment, and accessibility, potentially serving as discreet "pocket doctors" for sensitive health concerns. However, little is known about young women's perceptions and use of artificial intelligence for intimate health topics in such contexts.

methodsA cross-sectional quantitative study surveyed 525 female university students in Lebanon (ages 18-35) to assess their use, perceptions, drivers, and barriers related to artificial intelligence chatbots for intimate and general health concerns.

resultsThe study included 525 young Lebanese women with a mean age of 22.44 ± 3.74 years. Regarding AI chatbot use, the most common intimate health topics included menstrual problems (43.8%) and polycystic ovary syndrome (33.3%), while physical fitness (59.8%) and mental health (48.8%) were the predominant general health topics. The primary barriers to chatbot use were concerns about accuracy (85.5%) and lack of physical examination (85.3%), while key motivators included saving time (71.0%) and avoiding embarrassment (43.4%). Younger women were more likely to use artificial intelligence tools to avoid judgment and cost. Cluster analysis revealed distinct user profiles, including a super-user group with intensive engagement across sensitive health domains.

conclusionLarge language models serve as accessible, non-judgmental digital confidants for young Lebanese women's intimate health concerns, addressing socio-cultural stigma and healthcare system limitations. While promising, they should complement, not replace, professional care due to limitations in clinical reasoning, physical examination, and privacy concerns. Integrating artificial intelligence chatbots thoughtfully may enhance health information access and reduce barriers in stigmatized settings.

Indexed as

Artificial IntelligenceAdolescentAdultCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansLebanonSurveys and QuestionnairesYoung Adult

Identifiers

PMID41250005
PMCPMC12625598

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.