Large Language Models in Mental Health Care: A Systematic Scoping Review (Preprint) DOI
Yining Hua, Fenglin Liu, Kailai Yang

et al.

Published: July 8, 2024

BACKGROUND The integration of large language models (LLMs) in mental health care is an emerging field. There a need to systematically review the application outcomes and delineate advantages limitations clinical settings. OBJECTIVE This aims provide comprehensive overview use LLMs care, assessing their efficacy, challenges, potential for future applications. METHODS A systematic search was conducted across multiple databases including PubMed, Web Science, Google Scholar, arXiv, medRxiv, PsyArXiv November 2023. All forms original research, peer-reviewed or not, published disseminated between October 1, 2019, December 2, 2023, are included without restrictions if they used developed after T5 directly addressed research questions RESULTS From initial pool 313 articles, 34 met inclusion criteria based on relevance LLM robustness reported outcomes. Diverse applications identified, diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability reliability, nuanced handling states, effective evaluation methods. Despite successes accuracy accessibility improvement, gaps applicability ethical considerations were evident, pointing robust data, standardized evaluations, interdisciplinary collaboration. CONCLUSIONS hold substantial promise enhancing care. For full be realized, emphasis must placed developing datasets, development frameworks, guidelines, collaborations address current limitations.

Language: Английский

Large Language Models in Mental Health Care: A Systematic Scoping Review (Preprint) DOI
Yining Hua, Fenglin Liu, Kailai Yang

et al.

Published: July 8, 2024

BACKGROUND The integration of large language models (LLMs) in mental health care is an emerging field. There a need to systematically review the application outcomes and delineate advantages limitations clinical settings. OBJECTIVE This aims provide comprehensive overview use LLMs care, assessing their efficacy, challenges, potential for future applications. METHODS A systematic search was conducted across multiple databases including PubMed, Web Science, Google Scholar, arXiv, medRxiv, PsyArXiv November 2023. All forms original research, peer-reviewed or not, published disseminated between October 1, 2019, December 2, 2023, are included without restrictions if they used developed after T5 directly addressed research questions RESULTS From initial pool 313 articles, 34 met inclusion criteria based on relevance LLM robustness reported outcomes. Diverse applications identified, diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability reliability, nuanced handling states, effective evaluation methods. Despite successes accuracy accessibility improvement, gaps applicability ethical considerations were evident, pointing robust data, standardized evaluations, interdisciplinary collaboration. CONCLUSIONS hold substantial promise enhancing care. For full be realized, emphasis must placed developing datasets, development frameworks, guidelines, collaborations address current limitations.

Language: Английский

Citations

14