Evidence map›Paper›PMID 41462460›Full record

ArticleBMC health services research2025

Public attitudes and practices toward using AI chatbots for healthcare assistance: a multinational cross-sectional study.

Aya Elsayed Abdelwahed, Mahmoud Abd El-Nasser, Omar Qasem Heih, Aya Muhammed Suleiman, Ahmed Mithqal Khader, Rahma AbdElfattah Ibrahim, Mohamed Rabiea Abdelnaby Fathalla Hamad, Eslam Radwan, Azza Magdy Srour, HealthTech Alliance and 1 more

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Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

11 authors.

Aya Elsayed AbdelwahedFaculty of Medicine, Kafrelsheikh University, Kafr ash Shaykh, Egypt. aya1elsayed1@gmail.com.
Mahmoud Abd El-NasserFaculty of Medicine, Kafrelsheikh University, Kafr ash Shaykh, Egypt.
Omar Qasem HeihFaculty of Medicine and Health Sciences, Palestine Polytechnic University, Hebron, Palestine.
Aya Muhammed SuleimanFaculty of Medicine, Kafrelsheikh University, Kafr ash Shaykh, Egypt.
Ahmed Mithqal KhaderFaculty of Medicine and Health Sciences, Palestine Polytechnic University, Hebron, Palestine.
Rahma AbdElfattah IbrahimFaculty of Medicine, Kafrelsheikh University, Kafr ash Shaykh, Egypt.
Mohamed Rabiea Abdelnaby Fathalla HamadFaculty of Medicine, Kafrelsheikh University, Kafr ash Shaykh, Egypt.
Eslam RadwanFaculty of Medicine, Kafrelsheikh University, Kafr ash Shaykh, Egypt.
Azza Magdy SrourInternship student, Faculty of Medicine, KafrELShiek University, Kafr ash Shaykh, Egypt.
HealthTech Alliance
Marwa Mohammed Ibrahim GhallabDepartment of Medical Parasitology, Faculty of Medicine, Kafrelsheikh University, Kafr ash Shaykh, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUsing artificial intelligence (AI) chatbots in healthcare can enhance patient care. However, misuse may lead to negative outcomes. Our study’s aim is to evaluate the practices and attitudes related to AI chatbots for healthcare assistance within the general population in the Arab region.

methodsA population of 12 years old and above from 21 Arab countries was invited to complete a validated web-based questionnaire from 1 May to 1 June 2024. The survey consisted of four sections: demographics, identification, attitudes, and practices related to AI chatbots in healthcare assistance. We utilized Microsoft Excel and SPSS software for data entry and analysis. Descriptive statistics, chi-square tests, and binary logistic regression were used to analyze demographic associations and usage predictors for healthcare

resultsAmong the 12,886 valid responses, the median age was 24 years (IQR: 21–31), with a female-to-male ratio of 2:1. Most were single (66.8%), from Egypt (11.2%), urban residents (81.2%), students (43.6%), university-educated (73.2%), or healthcare-affiliated (40.2%). While 72.5% were aware of AI chatbots, only 26.4% used them, primarily for health coaching (67.5%), self-medication (54.5%), self-diagnosis (44.1%), and mental support (48%). ChatGPT was the most used chatbot (22.65%) for healthcare assistance. Individuals with psychological or mental health issues had greater odds of chatbot use (Exp(B) = 1.343, 95% CI: 1.189–1.516, p < 0.001), while the strongest predictor was participation in AI-related training courses, which was associated with more than a threefold increase in odds (Exp(B) = 3.109, 95% CI: 2.715–3.559, p < 0.001).

conclusionThis study highlighted varying attitudes and patterns regarding the use of AI-powered chatbots for healthcare assistance, from consultation to self-diagnosis and medication. The insights from this study can help policymakers, researchers, developers and healthcare professionals integrate AI chatbots more effectively into the existing healthcare system. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Artificial IntelligenceHealth Knowledge, Attitudes, PracticeMedical AssistancePublic OpinionAdolescentAdultCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesYoung AdultArtificial intelligenceAttitudeChatbotsChatGPTHealthcarePractice

Identifiers

PMID41462460
PMCPMC12967010

What OpenQuestion holds

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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.