AI, Machine Learning and the DSM

AI, Machine Learning and the DSM: Awareness, Research and Resources

“Anything that could give rise to smarter-than-human intelligence—in the form of Artificial Intelligence, brain-computer interfaces, or neuroscience-based human intelligence enhancement – wins hands down beyond contest as doing the most to change the world. Nothing else is even in the same league.” — Eliezer Yudkowsky

Artificial Intelligence (AI), Machine Learning and DSM Research

Machine-Learning Neuroscience and Mental Disorders

Artificial Intelligence (AI) and the DSM
"The DSM, or Diagnostic and Statistical Manual of Mental Disorders, is a widely used classification system for mental health disorders. Artificial Intelligence (AI) is increasingly being integrated into various aspects of mental health care, including diagnosis, treatment, and research. However, the direct integration of AI into the DSM itself is a complex and contentious issue with several considerations:

• AI-Assisted Diagnosis: AI algorithms can aid in diagnosing mental health disorders by analyzing various data sources such as patient interviews, electronic health records, and behavioral data. These algorithms can help clinicians make more accurate and timely diagnoses, potentially improving the reliability and validity of DSM-based diagnoses.

• Precision Psychiatry: AI technologies enable the development of precision psychiatry approaches, which tailor treatments to individual patients based on their unique characteristics and needs. This personalized approach may challenge the one-size-fits-all diagnostic criteria outlined in the DSM.

• Data-driven Insights: AI algorithms can analyze large datasets to identify patterns and trends in mental health disorders, potentially leading to revisions or updates in the DSM based on empirical evidence. However, integrating AI-derived insights into the DSM would require careful consideration of ethical, methodological, and clinical implications.

• Ethical and Social Implications: Integrating AI into the DSM raises ethical concerns related to data privacy, algorithmic bias, and the potential for over-reliance on technology in mental health care. There are also broader societal implications, such as the impact on stigma and access to care.

• Regulatory Challenges: The integration of AI into the DSM would necessitate regulatory frameworks to ensure the safety, efficacy, and ethical use of AI technologies in mental health diagnosis and treatment.

• Dynamic Nature of AI: Unlike traditional diagnostic criteria, which are periodically revised in new editions of the DSM, AI algorithms are continuously evolving based on new data and updates. This dynamic nature presents challenges in standardizing AI-driven diagnostic tools within a static classification system like the DSM.

Overall, while AI holds promise for improving the diagnosis and treatment of mental health disorders, its integration into the DSM requires careful consideration of ethical, clinical, and societal implications. Collaboration between mental health professionals, AI researchers, policymakers, and other stakeholders is essential to navigate these complexities and ensure responsible use of AI in mental health care." (Source: ChatGPT 2024)

AI Language Models could help Diagnose Schizophrenia University College London

AI and Human Emotions Mental Health and Motivation

AI in Psychiatry: Things Are Moving Fast Psychiatric Times

AI Takes on the Challenges of Deep Learning and Psychiatric Diagnosis Medium

A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis NIH

An AI-based Decision Support System for Predicting Mental Health Disorder Springer Link

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works ScienceDirect

Application and Purpose of the DSM Mental Health and Motivation

Application of Artificial Intelligence in Mental Health ResearchGate

Artificial Intelligence: Deciphering the Links between Psychiatric Disorders and Neurodegenerative Disease MDPI

Artificial Intelligence as a Psychiatric Diagnostic Tool Instead of DSM PDF Download Alpa Psychiatry  

Artificial intelligence in Adolescents Mental Health Disorder Diagnosis, Prognosis, and Treatment Frontiers in Public Health

Augmenting DSM-5 Diagnostic Criteria with Self-Attention-based BiLSTM Models for Psychiatric Diagnosis ScienceDirect

Building the Precision Medicine for Mental Disorders via Radiomics/Machine Learning and Neuroimaging Frontiers in Neuroscience

Challenges for Artificial Intelligence in Recognizing Mental Disorders NIH

Impact of AI on Human Decision-Making Mental Health and Motivation

Improving Mental Health through Computational Neuroscience Brown University

Machine-Learning Models for Neuroscience and Mental Disorders Knight Foundation School of Computing and Information Sciences

Machine-Learning Models for Neuroscience and Mental Disorders Florida International University

Meet the Scientists who are Training AI to Diagnose Mental illness The Verge

Modern Views of Machine Learning for Precision Psychiatry DirectScience

Overview of AI in Diagnosis for Schizophrenia, Depression and Anxiety - Shiyu Zuo PDF Download Advances in Social Science, Education and Humanities Research

Revolutionizing Psychiatric Diagnostics: Integrating Artificial Intelligence into DSM-5 Enhancement ResearchGate

The Basics of Augmented Intelligence: Some Factors Psychiatrists Need to Know Now American Psychiatric Association

The Performance of Artificial Intelligence-driven Technologies in Diagnosing Mental Disorders: An Umbrella Review Nature

What can we learn about the Psychiatric Diagnostic Categories by Analysing Patients' lived experiences with Machine-Learning? BMC Psychiatry

Neuroscience and Artificial Intelligence Need Each Other - Video

🎓 Mental Health, Psychology and Relationship Resources