Summary of Applying and Evaluating Large Language Models in Mental Health Care: a Scoping Review Of Human-assessed Generative Tasks, by Yining Hua et al.
Applying and Evaluating Large Language Models in Mental Health Care: A Scoping Review of Human-Assessed Generative Tasks
by Yining Hua, Hongbin Na, Zehan Li, Fenglin Liu, Xiao Fang, David Clifton, John Torous
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This scoping review aimed to assess the effectiveness of large language models (LLMs) in mental health care, focusing on studies where LLMs were tested with human participants in real-world scenarios. A systematic search identified 17 articles that met the inclusion criteria, encompassing applications such as clinical assistance, counseling, therapy, and emotional support. However, evaluation methods were often non-standardized, limiting comparability and robustness. Concerns were raised about privacy, safety, and fairness, as well as reliance on proprietary models like OpenAI’s GPT series, which hinders transparency and reproducibility. While LLMs show potential in expanding mental health care access, especially in underserved areas, the current evidence does not fully support their use as standalone interventions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being tested to help with mental health issues, like depression or anxiety. These models can have conversations with people and give emotional support. But how well do they really work? A group of researchers looked at 17 studies that used these models in real-life situations. They found that many of the studies didn’t follow the same rules, which makes it hard to compare them. The researchers also worried about things like keeping personal information private, making sure people are safe, and being fair. Right now, these models aren’t good enough to be used on their own to help people with mental health issues. We need more studies that follow the same rules so we can figure out how well they really work. |
Keywords
» Artificial intelligence » Gpt
