Diagnostic accuracy of large language models in psychiatry.

Journal: Asian journal of psychiatry
PMID:

Abstract

INTRODUCTION: Medical decision-making is crucial for effective treatment, especially in psychiatry where diagnosis often relies on subjective patient reports and a lack of high-specificity symptoms. Artificial intelligence (AI), particularly Large Language Models (LLMs) like GPT, has emerged as a promising tool to enhance diagnostic accuracy in psychiatry. This comparative study explores the diagnostic capabilities of several AI models, including Aya, GPT-3.5, GPT-4, GPT-3.5 clinical assistant (CA), Nemotron, and Nemotron CA, using clinical cases from the DSM-5.

Authors

  • Omid Kohandel Gargari
    Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Iran.
  • Farhad Fatehi
    Monash University, Melbourne, Australia.
  • Ida Mohammadi
    Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran.
  • Shahryar Rajai Firouzabadi
    Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran.
  • Arman Shafiee
    Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran.
  • Gholamreza Habibi
    Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Iran.