Evidence map›Paper›PMID 41527073›Full record

Trial reportBMC psychiatry2026

Development and efficacy testing of an artificial intelligence enabled treatment package (eDOSTHI) for tobacco cessation: study protocol for a randomized controlled trial.

Kaushik Mukhopadhyay, Jayanta Mukhopadhyay, Sudeshna Sarkar, Soumya Kanti Ghosh, Soumyajit Das, Mahashweta Choudhury, Shreya Bhattacharya, Amandeep Kaur, Kumari Rina, Aparajita Guin and 6 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC 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

16 authors.

Kaushik MukhopadhyayDepartment of Pharmacology, AIIMS Kalyani, Kalyani, India.
Jayanta MukhopadhyayDepartment of Computer Science and Engineering, IIT Kharagpur, Kharagpur, India.
Sudeshna SarkarDepartment of Computer Science and Engineering, IIT Kharagpur, Kharagpur, India.
Soumya Kanti GhoshDepartment of Computer Science and Engineering, IIT Kharagpur, Kharagpur, India.
Soumyajit DasDepartment of Computer Science and Engineering, IIT Kharagpur, Kharagpur, India.
Mahashweta ChoudhuryDepartment of Psychiatry, AIIMS Kalyani, Kalyani, India.
Shreya BhattacharyaDepartment of Psychiatry, AIIMS Kalyani, Kalyani, India.
Amandeep KaurDepartment of Community Medicine and Family Medicine, AIIMS Kalyani, Kalyani, India.
Kumari RinaDepartment of Psychiatry, AIIMS Kalyani, Kalyani, India.
Aparajita GuinDepartment of College of Nursing, AIIMS Kalyani, Kalyani, India.
Sirshendu ChaudhuriDepartment of Community Medicine, IIPH, Hyderabad, India.
Kamini VermaDepartment of Psychiatry, AIIMS Kalyani, Kalyani, India.
Arghya PalDepartment of Psychiatry, AIIMS Kalyani, Kalyani, India.
Sukanto SarkarDepartment of Psychiatry, AIIMS Kalyani, Kalyani, India.
Deepsikha RayDepartment of Psychology, University of Calcutta, Kolkata, India.
Aniruddha BasuDepartment of Psychiatry, AIIMS Kalyani, Kalyani, India. aniruddha.psy@aiimskalyani.edu.in.ORCID 0000-0003-4120-9145

Funding

Indian Council of Medical Research GRANTATHON2/MH-3/2024-NCD-II
6 · The paper itself

Abstract

backgroundTobacco dependence poses a substantial public health challenge in our country. Moreover, there is a well-established association between tobacco use and other substances. Thus, there is a need to enable patients who wish to quit tobacco use by linking them to treatment services and guiding them through the treatment course. This study proposes using artificial intelligence (AI) methods to increase the reach and ease of treatment for tobacco cessation. METHODS/

designA role-based and cross-platform application named Electronic means of Decreasing Overuse of Substance like Tobacco - a Health promoting Intervention (eDOSTHI) will be developed in English and Bengali to cater to patients. After the development of the software and pilot testing, efficacy testing will be done on a sample of 220 patients (age:18-65 years) each in the intervention and control arm by means of randomized controlled trial followed by assessment at 4 and 24 weeks.

conclusionThis study protocol describes randomised controlled trial to evaluate a language compatible and culturally-adapted mobile application (eDOSTHI) that can help patients with tobacco use to obtain medical advice and achieve abstinence. ETHICS AND DISSEMINATION: The study has ethical clearance (Ref No. IEC/AIIMS/Kalyani/Meeting/2023/013) from AIIMS Kalyani Institutional Ethics Committee (IEC). The norms of National Ethical Guideline for Biomedical and Health Research Involving Human Participants (2017) by the Indian Council for Medical Research (ICMR) for data collection and for collection of biological samples and storage will be adhered to. CTRI REGISTRATION: CTRI/2025/09/094053 Dated: 01.09.2025.

Indexed as

Artificial IntelligenceMobile ApplicationsTobacco Use CessationTobacco Use DisorderAdolescentAdultAgedFemaleHumansMaleMiddle AgedResearch DesignTobacco ControlYoung AdultArtificial intelligenceChatbotTobacco cessationVirtual assistance

Identifiers

PMID41527073
PMCPMC12874892

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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.