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Large language models outperform mental and medical health care professionals in identifying obsessive-compulsive disorder.
Kim, Jiyeong; Leonte, Kimberly G; Chen, Michael L; Torous, John B; Linos, Eleni; Pinto, Anthony; Rodriguez, Carolyn I.
Afiliación
  • Kim J; Stanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA. jykim3@stanford.edu.
  • Leonte KG; Clearview Horizons, North Andover, MA, USA.
  • Chen ML; Stanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA.
  • Torous JB; Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Linos E; Stanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA.
  • Pinto A; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
  • Rodriguez CI; Northwell, New Hyde Park, NY, USA.
NPJ Digit Med ; 7(1): 193, 2024 Jul 19.
Article en En | MEDLINE | ID: mdl-39030292
ABSTRACT
Despite the promising capacity of large language model (LLM)-powered chatbots to diagnose diseases, they have not been tested for obsessive-compulsive disorder (OCD). We assessed the diagnostic accuracy of LLMs in OCD using vignettes and found that LLMs outperformed medical and mental health professionals. This highlights the potential benefit of LLMs in assisting in the timely and accurate diagnosis of OCD, which usually entails a long delay in diagnosis and treatment.

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: NPJ Digit Med Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: NPJ Digit Med Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos