Benefits and Harms of Large Language Models in Digital Mental Health
- URL: http://arxiv.org/abs/2311.14693v1
- Date: Tue, 7 Nov 2023 14:11:10 GMT
- Title: Benefits and Harms of Large Language Models in Digital Mental Health
- Authors: Munmun De Choudhury, Sachin R. Pendse, Neha Kumar
- Abstract summary: Large language models (LLMs) show promise in leading digital mental health to uncharted territory.
This article presents contemporary perspectives on the opportunities and risks posed by LLMs in the design, development, and implementation of digital mental health tools.
- Score: 40.02859683420844
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The past decade has been transformative for mental health research and
practice. The ability to harness large repositories of data, whether from
electronic health records (EHR), mobile devices, or social media, has revealed
a potential for valuable insights into patient experiences, promising early,
proactive interventions, as well as personalized treatment plans. Recent
developments in generative artificial intelligence, particularly large language
models (LLMs), show promise in leading digital mental health to uncharted
territory. Patients are arriving at doctors' appointments with information
sourced from chatbots, state-of-the-art LLMs are being incorporated in medical
software and EHR systems, and chatbots from an ever-increasing number of
startups promise to serve as AI companions, friends, and partners. This article
presents contemporary perspectives on the opportunities and risks posed by LLMs
in the design, development, and implementation of digital mental health tools.
We adopt an ecological framework and draw on the affordances offered by LLMs to
discuss 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. We engage in a
thoughtful consideration of whether and how LLM-based technologies could or
should be employed for enhancing mental health. The benefits and harms our
article surfaces could serve to help shape future research, advocacy, and
regulatory efforts focused on creating more responsible, user-friendly,
equitable, and secure LLM-based tools for mental health treatment and
intervention.
Related papers
- Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health [1.8772687384996551]
Large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI.
We showcase the power of LLMs in handling unstructured conversational data through four case studies.
arXiv Detail & Related papers (2024-06-19T16:02:04Z) - A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions [31.04135502285516]
Large language models (LLMs) have received substantial attention due to their impressive capabilities for generating and understanding human-level language.
LLMs have emerged as an innovative and powerful adjunct in the medical field, transforming traditional practices and heralding a new era of enhanced healthcare services.
arXiv Detail & Related papers (2024-06-06T03:15:13Z) - Large Language Model for Mental Health: A Systematic Review [2.9429776664692526]
Large language models (LLMs) have attracted significant attention for potential applications in digital health.
This systematic review focuses on their strengths and limitations in early screening, digital interventions, and clinical applications.
arXiv Detail & Related papers (2024-02-19T17:58:41Z) - Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare.
The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body.
HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed.
Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
arXiv Detail & Related papers (2024-01-22T03:17:41Z) - Large Language Models in Mental Health Care: a Scoping Review [28.635427491110484]
The integration of large language models (LLMs) in mental health care is an emerging field.
There is a need to systematically review the application outcomes and delineate the advantages and limitations in clinical settings.
This review aims to provide a comprehensive overview of the use of LLMs in mental health care, assessing their efficacy, challenges, and potential for future applications.
arXiv Detail & Related papers (2024-01-01T17:35:52Z) - Challenges of Large Language Models for Mental Health Counseling [4.604003661048267]
The global mental health crisis is looming with a rapid increase in mental disorders, limited resources, and the social stigma of seeking treatment.
The application of large language models (LLMs) in the mental health domain raises concerns regarding the accuracy, effectiveness, and reliability of the information provided.
This paper investigates the major challenges associated with the development of LLMs for psychological counseling, including model hallucination, interpretability, bias, privacy, and clinical effectiveness.
arXiv Detail & Related papers (2023-11-23T08:56:41Z) - Redefining Digital Health Interfaces with Large Language Models [69.02059202720073]
Large Language Models (LLMs) have emerged as general-purpose models with the ability to process complex information.
We show how LLMs can provide a novel interface between clinicians and digital technologies.
We develop a new prognostic tool using automated machine learning.
arXiv Detail & Related papers (2023-10-05T14:18:40Z) - Privacy-preserving machine learning for healthcare: open challenges and
future perspectives [72.43506759789861]
We conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare.
We primarily focus on privacy-preserving training and inference-as-a-service.
The aim of this review is to guide the development of private and efficient ML models in healthcare.
arXiv Detail & Related papers (2023-03-27T19:20:51Z) - Mental Illness Classification on Social Media Texts using Deep Learning
and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected.
Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD.
This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z) - MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos.
We release a new dataset, MEDICA, for mental health patient engagement detection.
arXiv Detail & Related papers (2020-11-17T15:18:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.