ArticleFrontiers in psychiatry2023
Digital twins and the future of precision mental health.
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analyzing metaverse-based digital therapies, their effectiveness, and potential risks in mental healthcare.Scientific reports · 2025Pooled it
- The Mind From Within: Visceral Roots of Human Cognition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Digital doppelgangers in psychiatry.Discover mental health · 2026Review
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Designing a Digital Twin for the Management of Noncommunicable Diseases: Protocol for a Pilot Study and Methodology Validation.JMIR research protocols · 2026Article
- Positive psychology 4.0: a science of consequence for a world that can no longer wait.Frontiers in psychology · 2026Article
- Advanced biomaterials and digital twins for precision psychiatry: neuroimmune modulation and AI-guided therapeutics.Frontiers in bioengineering and biotechnology · 2026Article
- Digital Twin Brain: Generating Multitask Behavior from Connectomes for Personalized Therapy.BME frontiers · 2026Article
- Mapping the future of medicine through digital twins.Frontiers in molecular medicine · 2026Review
- Knowledge graphs in psychiatric research: Potential applications and future perspectives.Acta psychiatrica Scandinavica · 2025Review
- Randomized controlled study of a digital data driven intervention for depressive and generalized anxiety symptoms.NPJ digital medicine · 2025Article
- Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine.NPJ digital medicine · 2025Review
- Evaluating CFIR 2.0 in identifying digital twin implementation challenges in healthcare: bridging the dichotomy between engineering and healthcare communities.Frontiers in digital health · 2025Article
- Human centered design of AI-powered Digital Therapeutics for stress prevention: Perspectives from multi-stakeholders' workshops about the SHIVA solution.Internet interventions · 2024Article
- Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.Journal of medical Internet research · 2024Article
- Whole Person Modeling: a transdisciplinary approach to mental health research.Discover mental health · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Science faces challenges in developing much-needed precision mental health treatments to accurately identify and diagnose mental health problems and the optimal treatment for each individual. Digital twins (DTs) promise to revolutionize the field of mental health, as they are doing in other fields of science, including oncology and cardiology, where they have been successfully deployed. The use of DTs in mental health is yet to be explored. In this Perspective, we lay the conceptual foundations for mental health DTs (MHDT). An MHDT is a virtual representation of an individual's mental states and processes. It is continually updated from data collected over the lifespan of the individual, and guides mental health professionals in diagnosing and treating patients based on mechanistic models and statistical and machine learning tools. The merits of MHDT are demonstrated through the example of the working alliance between the therapist and the patient, which is one of the most consistent mechanisms predicting treatment outcome.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.