Evidence map›Paper›PMID 38239905›Full record

ArticleFrontiers in psychiatry2023

ChatGPT is not ready yet for use in providing mental health assessment and interventions.

Ismail Dergaa, Feten Fekih-Romdhane, Souheil Hallit, Alexandre Andrade Loch, Jordan M Glenn, Mohamed Saifeddin Fessi, Mohamed Ben Aissa, Nizar Souissi, Noomen Guelmami, Sarya Swed and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 5 pooled it
39.8field-weighted citation impact, top 1% of its field
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

58 citing papers in PubMed, 5 syntheses or guidelines pooled it, 98 citations in OpenAlex.

  1. Pooled it
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  6. Article
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  9. Artificial Intelligence-Assisted Generation of Case Scenarios and Multiple-Choice Questions in Psychiatry: A Pilot Study.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
    Article
  10. Explaining GPTs' schema of depression: A machine behavior analysis.Journal of psychopathology and clinical science · 2026
    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

13 authors at 12 institutions in 10 countries.

Ismail Dergaa *Primary Health Care Corporation (PHCC), Doha, Qatar.
Feten Fekih-Romdhane *The Tunisian Center of Early Intervention in Psychosis, Department of Psychiatry "Ibn Omrane", Razi Hospital, Manouba, Tunisia.
Souheil HallitSchool of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Alexandre Andrade LochLaboratorio de Neurociencias (LIM 27), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Instituto de Psiquiatria, Universidade de Sao Paulo, São Paulo, Brazil.
Jordan M GlennNeurotrack Technologies, Redwood City, CA, United States.
Mohamed Saifeddin FessiHigh Institute of Sport and Physical Education, University of Sfax, Sfax, Tunisia.
Mohamed Ben AissaDepartment of Human and Social Sciences, Higher Institute of Sport and Physical Education of Kef, University of Jendouba, Jendouba, Tunisia.
Nizar SouissiResearch Unit Physical Activity, Sport, and Health, UR18JS01, National Observatory of Sport, Tunis, Tunisia.
Noomen GuelmamiDepartment of Health Sciences (DISSAL), Postgraduate School of Public Health, University of Genoa, Genoa, Italy.
Sarya SwedFaculty of Medicine, Aleppo University, Aleppo, Syria.
Abdelfatteh El OmriSurgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, Qatar.
Nicola Luigi BragazziLaboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
Helmi Ben SaadService of Physiology and Functional Explorations, Farhat HACHED Hospital, University of Sousse, Sousse, Tunisia.
Hamad Medical Corporation · QAHoly Spirit University of Kaslik · LBHôpital Farhat Hached · TNHôpital Razi de La Manouba · TNHospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo · BRInstitut Supérieur du Sport et de l’Éducation Physique de Sfax · TNNeurotrack Technologies (United States) · USUniversity of Aleppo · SYUniversity of Genoa · ITUniversity of Jendouba · TNUniversity of Sfax · TNYork University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Psychiatry is a specialized field of medicine that focuses on the diagnosis, treatment, and prevention of mental health disorders. With advancements in technology and the rise of artificial intelligence (AI), there has been a growing interest in exploring the potential of AI language models systems, such as Chat Generative Pre-training Transformer (ChatGPT), to assist in the field of psychiatry. Objective: Our study aimed to evaluates the effectiveness, reliability and safeness of ChatGPT in assisting patients with mental health problems, and to assess its potential as a collaborative tool for mental health professionals through a simulated interaction with three distinct imaginary patients. Methods: Three imaginary patient scenarios (cases A, B, and C) were created, representing different mental health problems. All three patients present with, and seek to eliminate, the same chief complaint (i.e., difficulty falling asleep and waking up frequently during the night in the last 2°weeks). ChatGPT was engaged as a virtual psychiatric assistant to provide responses and treatment recommendations. Results: In case A, the recommendations were relatively appropriate (albeit non-specific), and could potentially be beneficial for both users and clinicians. However, as complexity of clinical cases increased (cases B and C), the information and recommendations generated by ChatGPT became inappropriate, even dangerous; and the limitations of the program became more glaring. The main strengths of ChatGPT lie in its ability to provide quick responses to user queries and to simulate empathy. One notable limitation is ChatGPT inability to interact with users to collect further information relevant to the diagnosis and management of a patient's clinical condition. Another serious limitation is ChatGPT inability to use critical thinking and clinical judgment to drive patient's management. Conclusion: As for July 2023, ChatGPT failed to give the simple medical advice given certain clinical scenarios. This supports that the quality of ChatGPT-generated content is still far from being a guide for users and professionals to provide accurate mental health information. It remains, therefore, premature to conclude on the usefulness and safety of ChatGPT in mental health practice.

Indexed as

anxietychatbotsdepressioninsomnialanguage modelsmental healthpatient carepsychiatric disorders

Identifiers

PMID38239905
PMCPMC10794665
OpenAlexW4390585437

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.