Large Language Model Applications in Mental Health: A Systematic Review

核心概念

LLMs show potential in mental health applications but face challenges like interpretability and ethical concerns.

要約

The content discusses the application of Large Language Models (LLMs) in mental health, focusing on early screening, digital interventions, and clinical applications. It provides insights into the strengths and limitations of LLMs, highlighting their effectiveness in classifying mental health issues but also addressing risks such as inconsistencies in generated text and ethical dilemmas. The systematic review covers various studies using LLMs for mental health chatbots, social media analysis, and other applications. Key findings include the potential of LLMs for personalized healthcare services but also emphasize the need for continued research to address challenges like bias and data privacy concerns.
Structure:

Abstract & Background
Objective & Methods
Results Analysis:

Mental Health Analysis Using Social Media Datasets
LLMs in Mental Health Chatbot
Other Applications of LLMs in Mental Health

Strengths and Limitations of Using LLMs in Mental Health
Discussion on Principal Findings, Limitations, Opportunities, and Future Work

統計

In total, 32 articles were evaluated.
LLMs exhibit substantial effectiveness in classifying and detecting mental health issues.
ChatGPT showed potential for mental health classifications with F1 scores of 0.73, 0.86, and 0.37.
RoBERTa model achieved a 78.85% F1-score for depression detection during the COVID-19 pandemic.

引用

"LLMs offer new possibilities for mental health care delivery."
"ChatGPT has shown promise for early intervention in mental illnesses."
"Ethical concerns regarding the use of LLMs in mental health care strategies are highlighted."

抽出されたキーインサイト

Large Language Model for Mental Health

by Zhijun Guo,A... 場所 arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15401.pdf

深掘り質問

How can interpretability be improved in LLM decision-making processes?

Interpretability in LLM decision-making processes can be enhanced through various approaches. One method is to incorporate explainable AI techniques such as attention mechanisms, which highlight the parts of the input that are most influential in making a prediction. By visualizing these attention weights, users can gain insights into how the model arrived at its decisions. Additionally, using modular architectures where different components of the model are designed to perform specific tasks can improve interpretability. This way, each module's function and contribution to the overall output become more transparent.
Another strategy is to provide detailed documentation of training datasets and methodologies used during model development. Sharing this information with stakeholders and conducting third-party audits on the models can increase transparency and trust in the decision-making process of LLMs. Moreover, creating standardized evaluation metrics for interpreting LLM outputs will help establish benchmarks for assessing model performance and reliability.

How can biases related to gender, race, ethnicity be mitigated when using LLMs for mental health applications?

Mitigating biases related to gender, race, and ethnicity when utilizing LLMs for mental health applications requires proactive measures throughout the entire development process. Firstly, it is essential to ensure diverse representation in both training data and research teams involved in building these models. By incorporating a wide range of perspectives during data collection and annotation stages, developers can reduce inherent biases present in datasets.
Regularly auditing datasets for bias detection and implementing techniques like debiasing algorithms or adversarial training methods can help mitigate discriminatory patterns within LLMs' decision-making processes. Furthermore, establishing clear guidelines on ethical considerations regarding sensitive attributes like gender or race is crucial for maintaining fairness in model predictions.
Continuous monitoring of model performance across different demographic groups through rigorous testing protocols will also aid in identifying any disparities early on. Lastly, fostering collaboration between domain experts from diverse backgrounds ensures that cultural nuances are considered when designing mental health interventions powered by LLMs.

How can data privacy concerns related to using LLMs be addressed?

Addressing data privacy concerns associated with employing LLMs involves implementing robust safeguards at various stages of deployment.
One approach is adopting privacy-preserving techniques such as federated learning or differential privacy that allow models to be trained without exposing raw user data directly.
Moreover,
utilizing secure computation protocols like homomorphic encryption enables computations on encrypted data without compromising confidentiality.
Implementing strict access controls
and encryption standards while storing sensitive information helps prevent unauthorized access.
Conducting regular security audits
and compliance checks ensures adherence
to stringent data protection regulations
such as GDPR or HIPAA.
Additionally,
providing transparent disclosures about
data usage practices,
obtaining explicit consent from users before collecting personal information,
and offering opt-out mechanisms further enhance trustworthiness around data handling procedures involving
LLMs