Evidence map›Paper›PMID 37612880›Full record

ReviewPsychiatry and clinical neurosciences2023

The now and future of ChatGPT and GPT in psychiatry.

Szu-Wei Cheng, Chung-Wen Chang, Wan-Jung Chang, Hao-Wei Wang, Chih-Sung Liang, Taishiro Kishimoto, Jane Pei-Chen Chang, John S Kuo, Kuan-Pin Su

Abstract readReview
In one paragraph

Review in Psychiatry and clinical neurosciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 4 pooled it
–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

65 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. The benefits and future potential of generative artificial intelligence (GAI) on mental health: a Delphi study.International journal of qualitative studies on health and well-being · 2026
    Article
  6. Article
  7. Rethinking AI in youth mental health: promise, perils, and ethical integration.International journal of mental health systems · 2026
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  16. Review
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  19. Artificial intelligence (AI) in psychotherapy: A challenging frontier.Australasian psychiatry : bulletin of Royal Australian and New Zealand College of Psychiatrists · 2025
    Article
  20. Article

5 more citing papers are in PubMed but not listed here.

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

9 authors.

Szu-Wei ChengCollege of Medicine, China Medical University, Taichung, Taiwan.ORCID https://orcid.org/0000-0002-4460-5517
Chung-Wen ChangDepartment of Information Management, Chia-Nan University of Pharmacy & Science, Tainan, Taiwan.
Wan-Jung ChangDepartment of Electronic Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan.ORCID https://orcid.org/0000-0002-7478-7315
Hao-Wei WangDepartment of Family Studies and Child Development, Shih Chien University, Taipei, Taiwan.
Chih-Sung LiangDepartment of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.ORCID https://orcid.org/0000-0003-1138-5586
Taishiro KishimotoHills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Tokyo, Japan.ORCID https://orcid.org/0000-0003-0557-8648
Jane Pei-Chen ChangCollege of Medicine, China Medical University, Taichung, Taiwan.ORCID https://orcid.org/0000-0001-5582-0928
John S KuoNeuroscience and Brain Disease Center, China Medical University, Taichung, Taiwan.ORCID https://orcid.org/0000-0001-6809-4806
Kuan-Pin SuCollege of Medicine, China Medical University, Taichung, Taiwan.ORCID https://orcid.org/0000-0002-4501-2502

Funding

China Medical University Hospital 106-227China Medical University Hospital 109-102China Medical University Hospital 109-244China Medical University Hospital 110-124China Medical University Hospital 111-245China Medical University Hospital 112-086China Medical University Hospital 112-097China Medical University Hospital 112-109China Medical University Hospital 112-232China Medical University Hospital DMR-106-101China Medical University Hospital DMR-HHC-109-11China Medical University Hospital HHC-109-12China Medical University Hospital HHC-110-10China Medical University Hospital HHC-111-8China Medical University, Taiwan 110-N-17China Medical University, Taiwan 1110-SR-73China Medical University, Taiwan CMU 110-AWARD-02Ministry of Education (MOE), Taiwan CMRC-CMA-2National Science and Technology Council 110-2314-B-039-029-MY3National Science and Technology Council 110-2320-B-039-047-MY3National Science and Technology Council 110-2320-B-039-048-MY2National Science and Technology Council 110-2321-B-006-004National Science and Technology Council 110-2811-B-039-507National Science and Technology Council 110-2813-C-039-327-BNational Science and Technology Council 111-2321-B-006-008National Science and Technology Council MOST 109-2320-B-038-057-MY3National Science and Technology Council NSTC 111-2314-B-039-041-MY3National Science and Technology Council NSTC 111-2314-B-039 -072 -MY3Tainan Municipal An-Nan Hospital 109-40Tainan Municipal An-Nan Hospital 110-13Tainan Municipal An-Nan Hospital 110-26Tainan Municipal An-Nan Hospital 110-44Tainan Municipal An-Nan Hospital 110-45Tainan Municipal An-Nan Hospital 111-27Tainan Municipal An-Nan Hospital 111-28Tainan Municipal An-Nan Hospital 111-47Tainan Municipal An-Nan Hospital 111-48Tainan Municipal An-Nan Hospital 111-52Tainan Municipal An-Nan Hospital ANHRF 109-31
6 · The paper itself

Abstract

ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of psychiatry have received limited attention. Deep learning has proven beneficial to psychiatry, and GPT is a powerful deep learning-based language model with immense potential for this field. Despite the convenience of ChatGPT, this advanced chatbot currently has limited practical applications in psychiatry. It may be used to support psychiatrists in routine tasks such as completing medical records, facilitating communications between clinicians and with patients, polishing academic writings and presentations, and programming and performing analyses for research. The current training and application of ChatGPT require using appropriate prompts to maximize appropriate outputs and minimize deleterious inaccuracies and phantom errors. Moreover, future GPT advances that incorporate empathy, emotion recognition, personality assessment, and detection of mental health warning signs are essential for its effective integration into psychiatric care. In the near future, developing a fully-automated psychotherapy system trained for expert communication (such as psychotherapy verbatim) is conceivable by building on foundational GPT technology. This dream system should integrate practical 'real world' inputs and friendly AI user and patient interfaces via clinically validated algorithms, voice comprehension/generation modules, and emotion discrimination algorithms based on facial expressions and physiological inputs from wearable devices. In addition to the technology challenges, we believe it is critical to establish generally accepted ethical standards for applying ChatGPT-related tools in all mental healthcare environments, including telemedicine and academic/training settings.

Indexed as

PsychiatryAlgorithmsEmotionsEmpathyHumansLanguageartificial intelligenceChatGPTdeep learningGPTinformatics and telecommunications in psychiatry

Identifiers

PMID37612880
PMCPMC10952959

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.