Evidence map›Paper›PMID 41214006›Full record

ArticleScientific reports2025

A comparative evaluatıon of AI-based ChatGPT and physicians in psychiatric case assessment.

Didem Umutlu, Yaşam Umutlu, Ayşe Döndü

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Didem Umutlu *Aydin Ataturk State Hospital, Aydin, Turkey.ORCID http://orcid.org/0000-0001-5278-9929
Yaşam UmutluAydın Efeler District Health Directorate, Aydin, Turkey.ORCID http://orcid.org/0000-0002-1848-6104
Ayşe Döndü *Department of Psychiatry, School of Medicine, Aydin Adnan Menderes University, Aydin, Türkiye. ayse.dondu@adu.edu.tr.ORCID http://orcid.org/0000-0003-0412-365X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

According to the results of our study, ChatGPT shows a variable performance in different aspects in medical fields. It was observed that it did not reach the level of physicians in communicating with the patient and conducting in-depth questioning. However, in most cases, the individuals' past psychiatric illnesses or medical histories are questioned. However, especially in cases where risk assessment is required, this process does not achieve the expected level of success and lacks the depth required for an effective assessment. While it functions effectively to inform differential diagnoses, its ability to make complex clinical decisions and make definitive diagnoses is limited. While it generally performed adequately in determining which medications should be prescribed and appropriate dosages, its capacity to warn about side effects of medications was found to be relatively weak. ChatGPT, which performs strongly in providing supportive treatment recommendations, shows a marked inability in more complex clinical processes such as making patient hospitalization decisions. These results suggest that AI-based systems can be useful as assistive tools in medical practice, but should be used with an awareness of their limitations.

Indexed as

Artificial IntelligenceMental DisordersPhysiciansAdultClinical Decision-MakingFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedRisk AssessmentArtificial intelligenceChatGPTClinical decision making

Identifiers

PMID41214006
PMCPMC12603154

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.