Applications of large language models in depression treatment: a systematic review

Authors

  • Maurício Rodrigues Lima UFG
  • Deller James Ferreira UFG
  • Elisângela Silva Dias UFG

DOI:

https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1318

Keywords:

Mental Health, Depression, Large Language Models

Abstract

Objective: This study reviews the use of Large Language Models (LLMs) in the field of mental health, specifically focusing on the treatment of depression. Method: A total of 18 articles out of an initial 121 were analyzed, exploring how LLMs assist in clinical decision- making and interaction between mental health professionals and depressed patients. Results: The main findings show that LLMs can increase accuracy in detecting symptoms and enhance therapeutic interventions through advanced conversational interfaces. Conclusion: The summary highlights gaps in existing research and emphasizes the study's contribution to a better understanding of the applicability of LLMs in clinical contexts. 

Author Biographies

Maurício Rodrigues Lima, UFG

Especialista em Engenharia de Software, Instituto de Informática, UFG, Goiânia (GO), Brasil.

Deller James Ferreira, UFG

Professora Dra, Instituto de Informática, UFG, Goiânia (GO), Brasil.

Elisângela Silva Dias, UFG

Professora Dra, Instituto de Informática, UFG, Goiânia (GO), Brasil.

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Published

2024-11-19

How to Cite

Lima, M. R., Ferreira, D. J., & Dias, E. S. (2024). Applications of large language models in depression treatment: a systematic review. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1318

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