Artificial Intelligence in Obsessive-Compulsive Disorder: A Systematic Review

Purpose of Review Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic...

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Published inCurrent treatment options in psychiatry Vol. 12; no. 1; p. 23
Main Authors Kim, Jiyeong, Pacheco, Juan Pablo Gonzalez, Golden, Ashleigh, Aboujaoude, Elias, van Roessel, Peter, Gandhi, Aayushi, Mukunda, Pavithra, Avanesyan, Tatevik, Xue, Haopeng, Adeli, Ehsan, Kim, Jane Paik, Saggar, Manish, Stirman, Shannon Wiltsey, Kuhn, Eric, Supekar, Kaustubh, Pohl, Kilian M., Rodriguez, Carolyn I.
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.01.2025
Springer Nature B.V
Subjects
Algorithms
Artificial intelligence
Behavior
Biomarkers
Bipolar disorder
Clinical outcomes
Comorbidity
Drug therapy
Electroconvulsive therapy
Human subjects
Language
Medicine
Medicine & Public Health
Mental health care
Natural language processing
Neurology
Neuroses
Obsessive compulsive disorder
Physiology
Psychiatry
Review
Schizoaffective disorder
Schizophrenia
Side effects
Social networks
Systematic review
Topical Collection on AI in Mental Health Care
United States > US
AI
OCD
Obsessive-compulsive disorder
Treatment
Diagnosis
Mental health
Online AccessGet full text
ISSN2196-3061
2196-3061
DOI10.1007/s40501-025-00359-8

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Abstract Purpose of Review Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery. Recent Findings While previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications. Summary AI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care.
AbstractList Purpose of Review Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery. Recent Findings While previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications. Summary AI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care.
Purpose of ReviewObsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery.Recent FindingsWhile previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications.SummaryAI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care.
Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery. While previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications. AI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care. The online version contains supplementary material available at 10.1007/s40501-025-00359-8.
Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery.Purpose of ReviewObsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery.While previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications.Recent FindingsWhile previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications.AI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care.SummaryAI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care.The online version contains supplementary material available at 10.1007/s40501-025-00359-8.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40501-025-00359-8.
ArticleNumber 23
Author Kuhn, Eric
Kim, Jiyeong
Kim, Jane Paik
Pacheco, Juan Pablo Gonzalez
van Roessel, Peter
Adeli, Ehsan
Pohl, Kilian M.
Avanesyan, Tatevik
Golden, Ashleigh
Stirman, Shannon Wiltsey
Supekar, Kaustubh
Gandhi, Aayushi
Saggar, Manish
Xue, Haopeng
Aboujaoude, Elias
Mukunda, Pavithra
Rodriguez, Carolyn I.
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  email: carolynrodriguez@stanford.edu
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Issue 1
Keywords AI
OCD
Obsessive-compulsive disorder
Treatment
Diagnosis
Mental health
Language English
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Snippet Purpose of Review Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its...
Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there...
Purpose of ReviewObsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its...
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SubjectTerms Algorithms
Artificial intelligence
Behavior
Biomarkers
Bipolar disorder
Clinical outcomes
Comorbidity
Drug therapy
Electroconvulsive therapy
Human subjects
Language
Medicine
Medicine & Public Health
Mental health care
Natural language processing
Neurology
Neuroses
Obsessive compulsive disorder
Physiology
Psychiatry
Review
Schizoaffective disorder
Schizophrenia
Side effects
Social networks
Systematic review
Topical Collection on AI in Mental Health Care
Title Artificial Intelligence in Obsessive-Compulsive Disorder: A Systematic Review
URI https://link.springer.com/article/10.1007/s40501-025-00359-8
https://www.ncbi.nlm.nih.gov/pubmed/40524733
https://www.proquest.com/docview/3218705050
https://www.proquest.com/docview/3219325371
https://pubmed.ncbi.nlm.nih.gov/PMC12167270
Volume 12
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