# Benefits and Harms of Large Language Models in Digital Mental Health

**Authors:** Munmun De Choudhury, Sachin R. Pendse, Neha Kumar  
**arXiv:** [2311.14693](https://arxiv.org/abs/2311.14693)  
**Date:** 2023-11-07  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.14693v1/

## Key points

- Significant advancements in mental health research and practice in the past decade
- Ability to analyze large amounts of data from electronic health records, mobile devices, and social media
- Valuable insights into patient experiences and early interventions enabled
- Generative artificial intelligence, particularly large language models (LLMs), revolutionizing digital mental health
- LLMs incorporated into medical software and electronic health record systems
- Patients arriving at doctors' appointments armed with information sourced from chatbots
- Opportunities and risks associated with LLMs in digital mental health tools
- Ecological framework approach with four application areas: care-seeking behaviors, community care provision, institutional and medical care provision, larger care ecologies at societal level
- Importance of responsible use of LLM-based tools by advocating for user-friendly, equitable, secure, and regulated applications.

## Summary

The past decade has seen significant advancements in mental health research and practice, thanks to the ability to analyze large amounts of data from sources such as electronic health records, mobile devices, and social media. This has provided valuable insights into patient experiences and enabled early interventions and personalized treatment plans. One area that shows promise in revolutionizing digital mental health is generative artificial intelligence, particularly large language models (LLMs). These LLMs are being incorporated into medical software and electronic health record systems, with patients even arriving at doctors' appointments armed with information sourced from chatbots. This article explores the opportunities and risks associated with LLMs in the design, development, and implementation of digital mental health tools. It takes an ecological framework approach and discusses four application areas: care-seeking behaviors from individuals in need of care, community care provision, institutional and medical care provision, and larger care ecologies at the societal level. The authors carefully consider whether and how LLM-based technologies should be employed to enhance mental health. While there are clear benefits to using LLMs in mental health treatment and intervention, there are also potential harms that need to be addressed. The article emphasizes the importance of responsible use of LLM-based tools by advocating for user-friendly, equitable, secure, and regulated applications.