AI alignment for mental health supports

post by hiki_t · 2025-02-24T04:21:42.379Z · LW · GW · 0 comments

Contents

  Initial Draft on 23 February
  Goals
  Motivations
  Initial Plans
  Long-term Plans
  References
None
No comments

Initial Draft on 23 February

 

Goals

As an affiliate member of  Cajal.org, I^[1] would like to introduce a novel application of AI alignment as a tool for diagnosing, visualising, predicting, preventing and improving mental health symptoms in human users. 

 

Motivations

Globally, nearly a billion people (1 in 8) are disgnosed with mental disorders. Among them, anxiety disorders (31%) and depressive disorders (28.9%) are the the most prevalent. Despite the widespread nature of these conditions, 71% of those diagnosed do not receive adequate treatment, while only 2% of healthcare resources are allocated to mental health services (WHO, 2022). 

Mental health disorders are closely linked with suicidal behaviors and reduce life expectancy by 10-20 years compared to the general population. Addressing this crisis requires urgent impelentation of effective mental health interventions. However, challenges such as limitited community support, a shortage of mental health professionals, inssuficient funding, and complexity of sysmptoms and causal factors make solutions difficult to implement. 

Recent advancements in Large Language Models (LLMs) like ChatGPT, Claude, and Gemini present a potential opportunity. These AI systems offer 24/7 availability, cost-effective scalability, and continuous learning, making them a valuable tool for enhancing mental health support. Furthermore, healthy conversation 

 

Initial Plans

The first step is developing conversation-based AI systems for mental health care. Studies have already demonstrated that AI can diagnose depression symptoms with accuracy comparable to mental health professionals (Elyoseph, et al., 2024). 

Building on this, the next phase involves visualizing and mapping symptom variability into clusters based on similarities, co-occurrence, and treatment effectiveness. By organizing symptoms into meaningful patterns, treatment recommendations can be optimized, reducing redundancy and improving efficiency.

 

Long-term Plans

Once diagnosis and symptom visualization are refined, the system will scale to a broader population, integrating automation and long-term treatment planning. With continuous data collection and personalized care, AI-driven mental health support can become an effective solution for underserved communities.

 

References

• Elyoseph, Z., Levkovich, I., & Shinan-Altman, S. (2024). Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public. Family Medicine and Community Health, 12(Suppl 1), e002583.

1. ^{^}

   I am a Cognitive Neuroscientist, studying brain mechanisms of human behaviors, including, sleep and memory, face recognition, motivation, and motor control, by employing a variety of research approaches of human behavioral and fMRI analysis, large-scale analysis, Machine Learning modeling and AI safety.

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