Evidence map›Paper›PMID 42151822›Full record

ArticleBMC primary care2026

Assessing AI-generated smoking cessation advice for patient education in primary care.

Canan Tuz, Demet Merder, Bahar Urun Unal, Fethi Sada Zekey, Kubra Uyar Zekey, Tuncay Muge Alvur

Abstract read
In one paragraph

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

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0cells of the map it votes in
0citing papers in PubMed
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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

6 authors.

Canan TuzFamily Medicine Department, Kocaeli University Medical School, Umuttepe, Kocaeli, Turkey. drcanantuzz@gmail.com.
Demet MerderFamily Medicine Department, Health Sciences University, Izmir Tepecik Health Practice And Research Center, Konak, İzmi̇r, Turkey.
Bahar Urun UnalFamily Medicine Department, Medical School, Selçuk University, Selçuk, Konya, Turkey.
Fethi Sada ZekeyFamily Medicine Department, Bozok University Medical School, Yozgat, Turkey.
Kubra Uyar ZekeyFamily Medicine Department, Bozok University Medical School, Yozgat, Turkey.
Tuncay Muge AlvurFamily Medicine Department, Kocaeli University Medical School, Umuttepe, Kocaeli, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has emerged as a promising tool to support smoking cessation in primary care, particularly for populations underserved by traditional interventions. However, the quality of AI-generated smoking cessation advice remains understudied, especially in low-resource settings and among vulnerable groups such as adolescents.

objectiveThis study aims to evaluate AI-generated responses to smoking cessation questions for patient education in primary care, comparing different AI programs in terms of knowledge, readability, and quality.

methodsTen publicly accessible AI programs were prompted in Turkish with 24 standardized, open-ended smoking cessation questions framed as a patient consultation. Two family medicine specialists independently assessed each response's readability using Ateşman's Readability Index, reliability using the DISCERN instrument, and accuracy and motivational interviewing quality using a bespoke rubric and OARS (Open questions, Affirmations, Reflections, Summaries) framework. Inter-rater agreement was evaluated via intraclass correlation. Descriptive statistics were computed for readability scores, DISCERN ratings, and accuracy grades.

resultsAll AI programs provided at least partially correct answers to all questions. The average readability score was 54.90 (medium difficulty) according to Atesman's Index. The mean DISCERN score was 66 ± 5.2, indicating excellent quality. Three AI programs incorporated core motivational interviewing skills. The most accurately answered question concerned e-cigarettes' harm compared to traditional cigarettes, while medication advice was least evidence-based.

conclusionsFree AI chatbots deliver reliably accurate and moderately readable smoking cessation advice, supporting their potential role as patient education adjuncts in primary care-particularly for individuals with at least a high school education. Further research should compare AI-assisted versus clinician-led interventions on smoking cessation outcomes.

Indexed as

Artificial IntelligencePatient Education as TopicPrimary Health CareSmoking CessationComprehensionHumansMotivational InterviewingReproducibility of ResultsTurkeyArtificial intelligenceDigital healthDISCERNPatient educationPrimary careReadabilitySmoking cessation

Identifiers

PMID42151822
PMCPMC13198041

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.