Translational psychiatry · 2026
Artificial intelligence (AI) for virtual reality exposure therapy (VRET): A systematic review.
СтатьяСистематический обзорНазвание и аннотация: Перевод в очереди
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Bergsnev Kamilla, Sánchez Laws Ana Luisa
Аннотация
OBJECTIVE: This systematic review maps what is known about using artificial intelligence (AI) to tailor virtual reality exposure therapy (VRET) to better meet the needs of patients and therapists.
BACKGROUND: Exposure therapy is a well-supported treatment for fear- and anxiety-related disorders that works by exposing patients to feared or avoided stimuli. VRET can facilitate exposure that would otherwise be impractical. AI offers growing possibilities to personalize VRET, potentially improving its effectiveness.
INCLUSION CRITERIA: We included peer-reviewed journal articles published up to November 14, 2025. After screening 377 records, 23 articles were included for full review.
METHODS: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Databases searched were PsycINFO, Web of Science, Google Scholar, EMBASE, CINAHL, and MEDLINE.
RESULTS: Studies point to promising AI applications for VRET, including conversational AI, machine learning for outcome prediction, and methods to personalize cues and contexts. However, over half of the reviewed papers in machine learning (ML) set goals or evaluated results without therapist or patient involvement.
CONCLUSION: AI for VRET remains at an early stage. There are robust examples of best practices that integrate stakeholder perspectives, but future work should more consistently include therapists and patients early in design, development, and evaluation and should more closely integrate up-to-date theorizations on exposure/extinction. We hope this review encourages transdisciplinary collaboration in this rapidly evolving field.
BACKGROUND: Exposure therapy is a well-supported treatment for fear- and anxiety-related disorders that works by exposing patients to feared or avoided stimuli. VRET can facilitate exposure that would otherwise be impractical. AI offers growing possibilities to personalize VRET, potentially improving its effectiveness.
INCLUSION CRITERIA: We included peer-reviewed journal articles published up to November 14, 2025. After screening 377 records, 23 articles were included for full review.
METHODS: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Databases searched were PsycINFO, Web of Science, Google Scholar, EMBASE, CINAHL, and MEDLINE.
RESULTS: Studies point to promising AI applications for VRET, including conversational AI, machine learning for outcome prediction, and methods to personalize cues and contexts. However, over half of the reviewed papers in machine learning (ML) set goals or evaluated results without therapist or patient involvement.
CONCLUSION: AI for VRET remains at an early stage. There are robust examples of best practices that integrate stakeholder perspectives, but future work should more consistently include therapists and patients early in design, development, and evaluation and should more closely integrate up-to-date theorizations on exposure/extinction. We hope this review encourages transdisciplinary collaboration in this rapidly evolving field.