A case study demonstrating applications of ChatGPT in the clinical management of treatment-resistant schizophrenia.
Open accessAbstract
Chat Generative Pre-trained Transformer, also known as ChatGPT, is a new artificial intelligence (AI) program that responds to user inquiry with discourse resembling human language. The range of ChatGPT capabilities caught the interest of the medical world after it demonstrated its ability to pass medical boards examinations. In this case report, we present the clinical treatment of a 22-year-old male diagnosed with treatment-resistant schizophrenia (TRS) and compare the medical management suggested by ChatGPT to current standards of care in order to assess the program's ability to identify the disorder, evaluate potential medical and psychiatric work-up, and develop a treatment plan addressing the distinct nuances of our patient. In our inquiry with ChatGPT, we found that it can accurately identify our patient as having TRS and order appropriate tests to methodically rule out alternative causes of acute psychosis. Furthermore, the AI program suggests pharmacologic treatment options including clozapine with adjuvant medications, and nonpharmacologic treatment options including electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), and psychotherapy which align with current standards of care. Lastly, ChatGPT provides a comprehensive list of side effects associated with antipsychotics and mood stabilizers used to treat TRS. We found both potential for and limitations in the clinical application of ChatGPT to assist in the assessment and management of complex medical conditions. Overall, ChatGPT may serve as a powerful tool to organize medical data in a meaningful and palatable format for medical professionals to reference during patient care.
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To obtain desired and appropriate information from ChatGPT, users require a minimum clinical knowledge that novice clinicians and people from non-medical fields do not have.
“These findings suggest, to obtain the desired and appropriate information from ChatGPT, users require a minimum clinical knowledge that novice clinicians and people from non-medical fields do not have.”
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- Authors
- Galido, P. V., Butala, S., Chakerian, M., & Agustines, D.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Galido, Pearl Valentine
- Venue
- Cureus
- Published
- 2023-04-26
- Volume
- 15
- Issue
- 4
- Pages
- e38166-e38166
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC10219639
- OpenAlex
- W4367060129
- Cited by
- 29
- References
- 16
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 14 · 2023: 4
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Cureus (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- ChatGPT, treatment-resistant schizophrenia, large language models in medicine, clinical decision support, clozapine, electroconvulsive therapy, repetitive transcranial magnetic stimulation, antipsychotic adverse effects, mood stabilizers, differential diagnosis of psychosis, treatment planning
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