Summary of Large Language Models in Mental Health Care: a Scoping Review, by Yining Hua et al.
Large Language Models in Mental Health Care: a Scoping Review
by Yining Hua, Fenglin Liu, Kailai Yang, Zehan Li, Hongbin Na, Yi-han Sheu, Peilin Zhou, Lauren V. Moran, Sophia Ananiadou, Andrew Beam, John Torous
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The integration of large language models (LLMs) in mental health care is an emerging field with a need for systematic review. This paper aims to provide a comprehensive overview of the use of LLMs in mental health care, assessing their efficacy, challenges, and potential applications. The authors conducted a systematic search across multiple databases and included 34 articles that met the inclusion criteria based on relevance to LLM application in mental health care. The paper identifies diverse applications of LLMs, including diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability and reliability, nuanced handling of mental states, and effective evaluation methods. Despite successes in accuracy and accessibility improvement, gaps in clinical applicability and ethical considerations were evident, pointing to the need for robust data, standardized evaluations, and interdisciplinary collaboration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are being used to help people with mental health issues. Researchers looked at lots of studies that used these models to see how well they worked. They found many ways LLMs can be helpful, like diagnosing problems or helping patients engage more in therapy. But the authors also found some challenges, such as making sure the data is good and handling emotions accurately. Despite some successes, there are still some limitations and concerns about using these models in real-life situations. |
