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Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health
Open accessAbstract
This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.
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“their effectiveness is poor, with between a third and two-thirds of those receiving interventions not having symptom reductions. 237”
From The Lancet Psychiatry Commission on youth mental health (2024) · cites this paper as [237]
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“the rapid development and adoption of breakthrough technologies including artificial intelligence, natural language processing, virtual and augmented reality, and virtual worlds provides an unprecedented opportunity to use new innovations to address historical problems in youth mental health.”
From The Lancet Psychiatry Commission on youth mental health (2024) · cites this paper as [237]
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Cites
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515 · cited 1×
“The Open Science Collaboration (2015) systematically sampled 100 results from three top-tier journals in psychology, and only 36% of the replication efforts yielded significant findings.”
BackgroundDissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“Some research has shown that inherent bias in original data sets has produced biased (racist) decisions (Obermeyer et al. 2019; Veale and Binns 2017).”
BackgroundPeeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
Kluger, A. N., & DeNisi, A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.2.254 · Cited by 5,882
Holmes, E. A., O'Connor, R. C., Perry, V. H., Tracey, I., Wessely, S., Arseneault, L., Ballard, C., Christensen, H., Cohen Silver, R., Everall, I., et al. (2020) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30168-1 · Cited by 5,715
Using Effect Size—or Why the P Value Is Not Enough
Sullivan, G. M., & Feinn, R. (2012) · Journal of Graduate Medical Education · doi:10.4300/jgme-d-12-00156.1 · Cited by 5,553
Chronic inflammation in the etiology of disease across the life span
Furman, D., Campisi, J., Verdin, E., Carrera-Bastos, P., Targ, S., Franceschi, C., Ferrucci, L., Gilroy, D. W., Fasano, A., Miller, G. W., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0675-0 · Cited by 4,915
CAUSALITY: MODELS, REASONING AND INFERENCE
Hitchcock, C., & Pearl, J. (2001) · The Philosophical Review · doi:10.2307/3182612 · Cited by 4,827
Flagged first, then by citations
Research works printed but not linked: 12
- Bickman, L. (1995). The Fort Bragg demonstration project: A managed continuum of care. The Child, Youth, and Family Services Quarterly,18(3), 2–5.
- Bickman, L., & Athay, M. I., (Eds.) (2012). Youth Mental Health Measurement (special issue) Administration and Policy in Mental Health and Mental Health Services Research, 39,1-2. ISSN: 0894-587X (Print) 1573-3289 (Online) · DOI
- Bickman, L., & Rog, D. (Eds.). (2016). The evaluation handbook: An evaluator’s companion. New York: Guilford Press.
- Heflinger, C. A., & Bickman, L. (1996). Family empowerment: A theoretically driven intervention and evaluation. In C. A. Heflinger & C. Nixon (Eds.), Families and mental health services for children and adolescents (pp. 96–116). Thousand Oaks: Sage.
- Institute of Medicine. (2007). IOM Roundtable on evidence-based medicine: The learning healthcare system: Workshop summary. Washington, DC: National Academies Press.
- King, R., & Bickman, L. (2017). Is there a future for therapists? Administration and Policy in Mental Health and Mental Health Services Research,44(5), 595–597. https://doi.org/10.1007/s10488-017-0814-1. · DOI
- Kohler, S. (2018). Precision medicine: Moving away from one-size-fits-all. Quest: Science for South Africa, 14(3). Academy of Science of South Africa (ASSAf)). Available at https://hdl.handle.net/20.500.11911/103
- Riemer, M., & Bickman, L. (2011). Using program theory to link social psychology and program evaluation. In M. M. Mark, S. I. Donaldson, & B. Campbell (Eds.), Social Psychology and Program/Policy Evaluation. New York: Guilford.
- Stroul, B. A., & Friedman, R. (1986). A system of care for children and youth with severe emotional disturbances (Rev ed.). Washington, DC: CASSP Technical Assistance Center, Georgetown University Child Development Center.
- Tan, J., Rollins, C. P. E., Israel, S., & Benrimoh, D. (2019). Primed for psychiatry: The role of artificial intelligence and machine learning in the optimization of depression treatment. University of Toronto Medical Journal,96(1), 43–47.
- An understanding of AI’s limitations is starting to sink in. (2020). Technology Quarterly: Artificial Intelligence and Its Limits, The Economist.
- Vinge, V. (1993). How to survive in the post-human era. In Interdisciplinary Science and Engineering in the Era of Cyberspace, Proceedings of the VISION-21 Symposium. NASA Conference Proceeding 10129. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19940022855.pdf
Cited by
Sorted by citations
Items 101–184 of 184
Mahon, D., Minami, T., & Brown, J. (2024) · Counselling and Psychotherapy Research · doi:10.1002/capr.12827 · Cited by 4
Chen, J., Yuan, L., Kang, S., Li, B., & Chow, D. (2024) · China Journal of Social Work · doi:10.1080/17525098.2024.2423950 · Cited by 3
“Despite this, much of the discourse on PMH remains theoretical, with a notable deficiency in empirical research, especially concerning service preferences – a critical element of PMH (Bickman 2020).”
InfluentialGlobal trends and hotspots of adolescent eating disorders: a bibliometric study and visual analysis
Ye, Q., Yang, Y., Qi, Y., Li, J., Lei, H., Sun, L., & Zhang, J. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1608930 · Cited by 3
Abundo, M. F., & Sav, A. (2025) · Healthcare · doi:10.3390/healthcare13172113 · Cited by 3
“This finding aligns with previous research conducted in hospital-based healthcare settings, showing that active participation is a strong predictor of intervention success [32,33].”
Patterns of service provision in child and adolescent mental health care in Australia
Salvador‐Carulla, L., Furst, M. A., Tabatabaei‐Jafari, H., Mendoza, J. A., Riordan, D. M., Moore, E., Rock, D. J., Anthes, L., Bagheri, N., & Salinas-Pérez, J. A. (2022) · Journal of Child Health Care · doi:10.1177/13674935221146381 · Cited by 3
“Since a system of care should consist of a wide range of services (Bickman, 2020), this imbalance may affect the ef fi ciency of the whole system.”
Hilty, D. M., Peled, A., & Luxton, D. D. (2023) · Springer eBooks · doi:10.1007/978-3-030-42825-9_152-1 · Cited by 3
Kilshaw, R. E., Boggins, A., Everett, O., Butner, E., Leifker, F. R., & Baucom, B. (2024) · JMIR Research Protocols · doi:10.2196/53857 · Cited by 3
“Until recently, the majority of this research has relied on methods and practices (eg, randomized controlled trials [1]) that require large amounts of human labor to recruit participants (eg, through in-person recruitment at clinics or calls to members of research registries) and collect the…”
Mehmood, R., Parvez, M. A., Qureshi, M., & Sagheer, Z. (2025) · Review of Applied Management and Social Sciences · doi:10.47067/ramss.v8i1.442 · Cited by 3
“In addition, these tools are cost-effective, and a modest price provides equivalent services as compared to traditional therapy (Bickman, 2020).”
Turner, E. H., & Mueller, C. W. (2025) · Community Mental Health Journal · doi:10.1007/s10597-024-01445-6 · Cited by 6
“PMHC is a major mechanism for serving the needs of youth with mental health difficulties and is particularly important for low resource and historically oppressed populations (Bickman, 2020).”
The impact of artificial intelligence in early disease detection: A review
Saratkar, S., Thute, T., Choudhari, A., Raut, R., & Thakare, G. (2025) · AIP conference proceedings · doi:10.1063/5.0241977 · Cited by 3
NAMS task force report on mental stress
Sagar, R., Chatterjee, K., Thareja, S., Timothy, A., Yadav, A. S., Yadav, P., Dhamija, R. K., Madhu, S., Kathiresan, P., Prasad, P., et al. (2025) · Annals of the National Academy of Medical Sciences (India) · doi:10.25259/anams_tfr_10_2024 · Cited by 3
The Gap in Between: Social Work Practice in the Midst of the Technological Era
Ho, J. C. K., & Ng, L. U. I. (2025) · Journal of Social Work and Social Welfare Policy · doi:10.33790/jswwp1100153 · Cited by 2
Emikönel, S., & TÜRKMEN, İ. (2025) · Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi · doi:10.34087/cbusbed.1653437 · Cited by 4
Scoping review of precision child and youth mental health research: dwelling in possibility
Lyeo, J. S., Blais, A. Z., Cloutier, P., Boafo, A., Dargél, A., Helleman, A., Tanya, T., Kashala-Abotnes, E., Honeywell, C. R., & PAJER, K. A. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1691548 · Cited by 2
Artificial intelligence in the psychologist’s toolkit: Psypilot as a case study
Roca, P., Zangri, R. M., Rodriguez-Fernandez, G., Sanchez-Pedreño, M., & del Valle, E. P. G. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1775464 · Cited by 4
“Psypilot is an AI-powered clinical assistance platform designed specifically for psychologists and mental health services to implement Precision Mental Health, promoting measurement-based care and data-driven decision-making.”
Performance of a Supervisor Observational Coding System and an Audit and Feedback Intervention
Chapman, J. E., Schoenwald, S. K., Sheidow, A. J., & Cunningham, P. B. (2022) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-022-01191-5 · Cited by 4
“The system is intended to support a “culture of feedback” (Bickman, 2020), and relative to supervisors in community settings who are newly exposed to EBT supervision, there may have been fewer obstacles related to the overall acceptability of monitoring and feedback or to engaging with a feedback…”
BackgroundBibliometric and Content Analysis of Articles on Artificial Intelligence in Healthcare
TÜRKMEN, İ., Söyler, A., Aliyev, S., & Semiz, T. (2024) · Journal of International Health Sciences and Management · doi:10.48121/jihsam.1533583 · Cited by 2
Dynamic Real‐Time Biosensing Enabled Biorhythm Tracking for Psychiatric Disorders
Huizer, K., Banga, I. K., Kumar, R. M., Muthukumar, S., & Prasad, S. (2024) · Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology · doi:10.1002/wnan.2021 · Cited by 2
Zeeshan, R., Bogue, J., Gill, A., & Asghar, M. N. (2025) · JMIR Human Factors · doi:10.2196/64919 · Cited by 2
“AI has become an increasingly active area of research over the past few years in precision mental health care [44,45].”
Unstructured NLP Model to Predict Suicidal Identification in the Adults
Nuthakki, R., Jain, A., Changala, R., Dhyani, B., & Hazela, B. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-4203-9.ch006 · Cited by 2
Baeza‐Hernandez, K., Lasecke, M., Herman, A. M., Kim, J., Lin, J. M. J., Dosovitsky, G., Bunge, E. L., & Park, A. L. (2022) · Evidence-Based Practice in Child and Adolescent Mental Health · doi:10.1080/23794925.2022.2081945 · Cited by 1
“Future research should investigate methods for improving the sustainability of implementing mental and behavioral health promotion programs (Sustainability phase), as well as for scaling the reach of such programs (e.g., through community-based programming and/or digital solutions; Bickman, 2020).”
MethodStrengths Model for Youth (SM-Y) Case Management: Initial Findings on Youth Outcomes
Mendenhall, A. N., Grube, W. A., & Jung, E. (2024) · Community Mental Health Journal · doi:10.1007/s10597-024-01265-8 · Cited by 1
Assessment and Intervention for Children with Autism: Are AI Technology Interventions Effective?
Shen, Y.-B., Yu, X., & Liao, X.-L. (2024) · International Conference on Educational Technology (ICET) · doi:10.1109/icet62460.2024.10869182 · Cited by 1
“…be summarized by the term "precision interventions", i.e., a shift from a one-size-fits-all model of diagnosis and treatment to one that is based on the collection of large amounts of data, personalized diagnosis of the patient, and the establishment of appropriate and effective interventions [13].”
InfluentialArtificial Intelligence in Healthcare: Navigating Security and Privacy Challenges
Çela, E., Fonkam, M. M., Vedishchev, A., & Vajjhala, N. R. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-84404-1_1 · Cited by 3
Günday, H. M., & Albayrak, E. (2025) · Psikiyatride Guncel Yaklasimlar - Current Approaches in Psychiatry · doi:10.18863/pgy.1620119 · Cited by 1
Williams, T. S., Taylor, M., Green, R. R., Lyon, R. E., Bondi, B. C., & Désiré, N. (2025) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/acaf061 · Cited by 1
Kumar, A., Bansal, R., Sharma, R. B., Sharma, A., & Maini, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3658-9.ch008 · Cited by 1
Singh, S., Kumar, A., & Singh, G. (2025) · Proceedings of the Indian National Science Academy · doi:10.1007/s43538-025-00665-y · Cited by 1
Kumar, A., Rawat, M., Bhatnagar, P., & Hussain, Z. (2025) · Springer eBooks · doi:10.1007/978-3-031-94086-6_1 · Cited by 2
Klein, A., Song, J., Chim, J., Keren, L., Triantafyllopoulos, A., Schuller, B., Liakata, M., & Atzil-Slonim, D. (2026) · JMIR Formative Research · doi:10.2196/71230 · Cited by 1
Artificial Intelligence Technology-Driven Teacher Mental State Assessment and Improvement Method
Qin, Y. (2024) · International Journal of Information and Communication Technology Education · doi:10.4018/ijicte.343309 · Cited by 1
Siddhanta, M., Kuriansky, J., & Barry, E. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-18529-8.00028-7 · Cited by 1
Hilty, D. M., Peled, A., & Luxton, D. D. (2024) · Springer eBooks · doi:10.1007/978-3-030-51366-5_152 · Cited by 1
Potenziale für die PTBS-Behandlung
Eberhardt, S. T., Schaffrath, J., & Lutz, W. (2024) · Trauma und Gewalt · doi:10.21706/tg-18-4-288 · Cited by 1
Artificial Intelligence, Virtual Reality, and Online Games
Bauman, S. A., & Rivers, I. (2023) · Springer eBooks · doi:10.1007/978-3-031-32122-1_7 · Cited by 2
Digital Interventions for Mental Health Problems
Zhang, H., & Fu, G. (2023) · International Journal of Managerial Studies and Research · doi:10.20431/2349-0349.1108007 · Cited by 1
“For example, AI therapists can serve patients 24 hours a day, and are not limited by time and place (Bickman Leonard, 2020), and they can contact people who cannot obtain mental health services in real life in a virtual way, use a simple and intuitive interface, interact with them in a familiar…”
MacIntyre, D. J. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009052023.003 · Cited by 1
Virtual Reality in Health SciencesPreprint
Castrillón-Arias, C. G., Caicedo-Eraso, J. C., & Serna, D. R. V. (2023) · Research Square · doi:10.21203/rs.3.rs-3157640/v1 · Cited by 1
Hikmat, R., Nilmanat, K., & Damkliang, J. (2026) · Journal of Mental Health · doi:10.1080/09638237.2026.2695971 · Cited by 1
“The use of artificial intelligence in mental health services is experiencing rapid development and is increasingly widespread across clinical, community, and digital service contexts (Bickman, 2020).”
Schoenwald, S. K., Bradshaw, C. P., Hoagwood, K., Atkins, M. S., Ialongo, N. S., & Douglas, S. R. (2020) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01070-x
“In his article entitled, Improving Mental Health Services: A 50 Year Journey from Randomized Experiments to Artificial Intelligence (Bickman 2020), Len highlights the potentially pivotal role that innovative technologies including artificial intelligence and telehealth could play in the delivery of mental health servi…”
BackgroundInyang, I. O., Gitau, S., & Osoro, E. (2020) · Journal of Research in Philosophy and History · doi:10.22158/jrph.v3n2p194
“In addressing mental health challenges resulting from trauma and other life events, Bickman (2020) is of the opinion that mental health service can be improved if evidence-based services are made available. According to the findings, fewer clients are exposed to effective evidence-based interventions that are sufficie…”
BackgroundInfluentialReform 2.0: Augmenting Innovative Mental Health Interventions
Bickman, L. (2021) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01107-1
“As I point out (Bickman 2020) our work with AI has a long way to go.”
BackgroundSelf-citationMachine learning methods for predicting postpartum depression: A scoping review (Preprint)Preprint
Saqib, K., Khan, A. F., & Butt, Z. A. (2021) · doi:10.2196/preprints.29765
Construction of College Students’ Mental Health Education Model Based on Data Analysis
Tang, F.-X. (2022) · Scientific Programming · doi:10.1155/2022/7044526
“However, in recent years many scholars believe that in addition to gradient descent algorithms that can effectively optimize the parameters in neural networks, population intelligence optimization algorithms can also train deep convolutional networks with more than 4 million parameters [5].”
BackgroundYang, R., & Wang, Y. (2022) · Occupational Therapy International · doi:10.1155/2022/4833968
“From adolescence to early adulthood, individuals are and will remain in a critical period of neurodevelopment for a long time, and this period is accompanied by dramatic changes in hormonal and emotional experiences [8].”
BackgroundCan Digital Technologies Change Schizophrenia Care?
de Almeida, R. S., & Silva, T. A. N. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-5260-8.ch005
Mlakar, I., Ariöz, U., Smrke, U., Plohl, N., Šafran, V., & Rojc, M. (2023) · doi:10.2196/preprints.46022
Promoting youth mental health through stakeholder-generated innovationPreprint
Park, A. L., Kim, J. J., Lin, J., Sanchez, A. T., Lee, P., & Gloner, M. C. B. (2023) · doi:10.31234/osf.io/nwbzx
Amutha, S. (2023) · International Journal of Computer Science and Engineering · doi:10.14445/23488387/ijcse-v10i3p101
Truth in myth: Ancient Stories' windows on treating modern trauma
Kieran, K. (2023) · Journal of Advanced Nursing · doi:10.1111/jan.15892
The current state and prospects of artificial intelligence in depression detection
Wan, Q., Hu, M., & Wang, Y. C. (2023) · International Symposium on Artificial Intelligence for Medicine Sciences (ISAIMS) · doi:10.1145/3644116.3644204
“This approach eliminates geographical and temporal constraints, allowing depressed patients to conveniently access professional advice and support [23].”
BackgroundBayrampour, H., & Lodo, L. (2024) · doi:10.2196/preprints.59388
Personalized Web Services Composition Using Artificial IntelligencePreprint
Chebbi, I., Abidi, S., & Ayed, L. J. B. (2024) · Research Square · doi:10.21203/rs.3.rs-4406066/v1
Need for Cultural Adaptations in Evidence Based Psychotherapies
Khan, A., Madihie, A., & Khan, R. U. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815274257124010008
Cojocaru, A., & Bubuioc, L. (2024) · Vector European · doi:10.52507/2345-1106.2024-2.34
An Intelligent Monitoring System for Sports Mental Health Status based on Big Data
Han, Y., & Geng, W. (2025) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v26i1.3744
Krishna, K. V. B., Reddy, P. N., Allakonda, M., Anjareddy, G., Shanthi, B., & Kuntala, R. (2025) · Cognitive science and technology · doi:10.1007/978-981-97-9266-5_59
Schmaling, K. B., & Kaplan, R. M. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009391733.012
Schmaling, K. B., & Kaplan, R. M. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009391733.006
Model of Mental Health Intervention based on Emotion Recognition - using Genetic Algorithm
Zhang, Y. (2024) · International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC) · doi:10.1109/icdscnc62492.2024.10939465
“To overcome the limitations of traditional methods, some researchers have begun to explore the use of genetic algorithms for emotion recognition models in mental health interventions [9].”
Li, Y., & He, R. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-8003-0_13
Bayrampour, H., & Loro, L. (2025) · International Symposium on Medical Information and Communication Technology · doi:10.1109/ismict64722.2025.11059425
“Given the prevalence of mental health conditions and the shortage of health care services worldwide, AI-based solutions may offer a scalable prospect to address this challenging dilemma [4].”
Chatterjee, S., Dindarian, A., & Rengaraju, U. (2025) · Apress eBooks · doi:10.1007/979-8-8688-1186-9_5
Artificial intelligence and mental health: Opportunities and challenges
Stankovska, G., & Ahmeti, A. (2025) · Przegląd Krytyczny · doi:10.14746/pk.2025.7.2.1
Vats, P., Tak, T. K., Upreti, K., Mahajan, S., Kshirsagar, P. R., & Upadhyay, G. M. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.110996
A Study on the Effectiveness of AI-powered Virtual Assistants in Student Support Services
Zhang, Y., & Xie, M. (2025) · International Conference on Educational Technology (ICET) · doi:10.1109/icet67421.2025.11380772
“On one hand, algorithmic dependency may lead to ideological biases in knowledge bases [4][5] , solidification of service models [6] , and weakened student autonomy [7] .”
Noheda, S., Ramírez-Riveros, E., Rodriguez‐Moreno, S., Martín-Azañedo, C., Georgescu, A., & Roca, P. (2026) · doi:10.2196/preprints.93893
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Dehankar, P., Chaturvedi, K., & Das, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407380
CogniAid: A Privacy-Preserving Conversational AI for Personalized Mental Health Care
Maheswari, M., Bora, D., Keerthana., S., Shivani., S., Abithashree.S, & Asitha.L (2026) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict68280.2026.11510968
Noheda, S., Ramírez-Riveros, E., Rodriguez‐Moreno, S., Martín-Azañedo, C., Georgescu, A., & Roca, P. (2026) · Journal of Medical Internet Research · doi:10.2196/93893
“However, among all these emerging technologies, artificial intelligence (AI) stands out as one of the most transformative innovations in mental health care, catalyzing a paradigm shift toward precision mental health [10].”
Inteligencia Artificial Aplicada a la Psicología: El Ejemplo de Psypilot Como Copiloto Terapéutico
Roca, P., Sanchez-Pedreño, M., Rodriguez-Fernandez, G., del Valle, E. P. G., & Zangri, R. M. (2026) · APUNTES DE PSICOLOGÍA · doi:10.70478/apuntes.psi.2026.44.09 · Cited by 1
Azeem, O., Zhang, Y., & Haidong, Z. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100322
Ebner, K., Hofer, A., Souček, R., & Spurk, D. (2026) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2026.104264
Zupančić, V., & Erjavec, K. (2026) · Pielegniarstwo XXI wieku / Nursing in the 21st Century · doi:10.12923/pielxxiw-2026-0022
Psychotherapeutic Competencies in Blended Care Psychotherapy: Development of a Competence Framework
Weiher, G. M., Woide, M. N., Zirenko, M. S., Tremmel, M., Stangier, U., & Horz, H. (2026) · JMIR Mental Health · doi:10.2196/100125
Herrera, L. J., & Roca, P. (2026) · Behaviour Research and Therapy · doi:10.1016/j.brat.2026.105140
Haroz, E. E., Bradshaw, C. P., & Feaster, D. J. (2026) · Prevention Science · doi:10.1007/s11121-026-01971-7
“…across populations that have been served poorly by static, single-instrument screening, and they can incorporate the multimodal, longitudinal, and contextual data that prevention research routinely generates but has often strug-gled to model jointly (Bickman, 2020; Mhasawade et al., 2021).”
Panicker, S. G., & Majumdar, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9109-0.ch006
Strength-Focused Treatment is Associated with Positive Outcomes in Implemented Multisystemic Therapy
Turner, E. H., Holmes, K. L., & Mueller, C. W. (2026) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-026-01523-9
Metadata
- Authors
- Bickman, L.
- Affiliations
- Florida International University (US)
- Corresponding author
- Bickman, Leonard
- ORCID
- Bickman, Leonard
- Venue
- Administration and Policy in Mental Health and Mental Health Services Research
- Published
- 2020-07-26
- Volume
- 47
- Issue
- 5
- Pages
- 795-843
- ISSN
- 0894-587X (ISSN-L)
- 1573-3289
- PMC
- PMC7382706
- OpenAlex
- W3045482196
- Cited by
- 184
- References
- 353
- Citations by year
- 2026: 23 · 2025: 48 · 2024: 36 · 2023: 30 · 2022: 27 · 2021: 14 · 2020: 6
- FWCI
- 17.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Administration and Policy in Mental Health and Mental Health Services Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Johns Hopkins Bloomberg School of Public Health
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Social Sciences › Psychology › Clinical Psychology › Psychiatric care and mental health services
- Keywords
- mental health services, artificial intelligence, precision mental health, personalized treatment, mental health services research, treatment effectiveness
- MeSH terms
- Artificial Intelligence, Health Services Research, Humans, Mental Health Services, Randomized Controlled Trials as Topic, Precision Medicine
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