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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
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
The Lancet Psychiatry Commission on youth mental health
McGorry, P. D., Mei, C., Dalal, N., Alvarez-Jimenez, M., Blakemore, S.-J., Browne, V., Dooley, B., Hickie, I. B., Jones, P. B., McDaid, D., et al. (2024) · The Lancet Psychiatry · doi:10.1016/s2215-0366(24)00163-9 · Cited by 663
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 247
“However, the amount of precision that can be modeled using ML is far beyond the first level.(181,182) During psychiatric evaluation, psychiatrists may try to build a mental model of what is going on in the patient’s life in about 30 min.”
BackgroundShahzad, M. F., Xu, S., Lim, W. M., Yang, X., & Khan, Q. R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29523 · Cited by 205
“AI and social media have woven themselves deeply into the fabric of modern life, redefining paradigms in sectors ranging from education to interpersonal communication [128].”
BackgroundGonzález, P. C., He, A., Lam, E. K. M., Ng, I. M. C., Li, M. W., Hou, R., Chan, J. N.-M., Sahni, Y., Viñas‐Guasch, N., Miller, T., et al. (2025) · Psychological Medicine · doi:10.1017/s0033291724003295 · Cited by 255
Squires, M., Tao, X., Elangovan, S., Gururajan, R., Zhou, X., Acharya, U. R., & Li, Y. (2023) · Brain Informatics · doi:10.1186/s40708-023-00188-6 · Cited by 188
“Thus, unsupervised algorithms can be used to identify groupings that transcend existing diagnostic labels [70].”
BackgroundInfluentialMeasurement-Based and Data-Informed Psychological Therapy
Lutz, W., Schwartz, B., & Delgadillo, J. (2021) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-071720-014821 · Cited by 146
A Survey on Wearable Sensors for Mental Health Monitoring
Gomes, N., Pato, M. P. M., Lourenço, A., & Datia, N. (2023) · Sensors · doi:10.3390/s23031330 · Cited by 161
Sadeh‐Sharvit, S., Camp, T. D., Horton, S. E., Hefner, J. D., Berry, J. M., Grossman, E., & Hollon, S. D. (2023) · Journal of Medical Internet Research · doi:10.2196/46781 · Cited by 129
“This reality makes it an opportune time to introduce digital tools like artificial intelligence (AI) platforms that can support therapists as they provide EBPs and seek to reduce workload [13].”
Attitudes and perspectives towards the preferences for artificial intelligence in psychotherapy
Aktan, M. E., Turhan, Z., & Dolu, İ. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107273 · Cited by 111
Machine Learning Methods for Predicting Postpartum Depression: Scoping Review
Saqib, K., Khan, A. F., & Butt, Z. A. (2021) · JMIR Mental Health · doi:10.2196/29838 · Cited by 93
“Thus, in addition to disease-model refinement, ML may benefit psychiatry by characterizing those at risk and personalizing and discovering pharmacological therapeutics [26,27].”
BackgroundWhat Is the Current and Future Status of Digital Mental Health Interventions?
Baños, R. M., Herrero, R., & Vara, M. D. (2022) · The Spanish Journal of Psychology · doi:10.1017/sjp.2022.2 · Cited by 82
“In this regard, precision medicine and AI offer the opportunity to improve existing therapeutic barriers (Bickman, 2020).”
BackgroundHinduja, S., Afrin, M., Mistry, S., & Krishna, A. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100113 · Cited by 61
Joshi, S., Sharma, M., Das, R. P., Rosak-Szyrocka, J., Żywiołek, J., Muduli, K., & Prasad, M. (2022) · Sustainability · doi:10.3390/su141811698 · Cited by 64
Nilsén, P., Svedberg, P., Nygren, J., Frideros, M., Johansson, J., & Schueller, S. M. (2022) · Implementation Research and Practice · doi:10.1177/26334895221112033 · Cited by 71
“This might introduce algorithm bias and models that fail to predict in speci fi c populations (Brodwin & Ross, 2021; Coley et al., 2021).”
MethodMachine learning in biosignals processing for mental health: A narrative review
Sajno, E., Bartolotta, S., Tuena, C., Cipresso, P., Pedroli, E., & Riva, G. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1066317 · Cited by 59
“Furthermore, in mental health, large datasets required for good AI predictions are rare, and a high drop-out rate hinders the collection of longitudinal data: this could result in a non-representative set of data (Bickman, 2020).”
BackgroundKellogg, K. C., & Sadeh‐Sharvit, S. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.990370 · Cited by 50
Deng, D., Rogers, T., & Naslund, J. A. (2023) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-023-00302-9 · Cited by 45
“With the advancement of data-driven precision health and artificial intelligence, there have been attempts to use automated triage to improve and prioritize moderator responsiveness and better protect those most vulnerable (Bickman, 2020; Fiske et al., 2019; Gooding & Kariotis, 2021).”
MethodTreatment Personalization and Precision Mental Health Care: Where are we and where do we want to go?
Moggia, D., Lutz, W., Brakemeier, E., & Bickman, L. (2024) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-024-01407-w · Cited by 40
“Nevertheless, with the advent of big data, machine learning, and artificial intelligence in recent years, personaliza - tion has moved towards the concept of precision mental health care (Bickman, 2020; Bickman et al., 2016 ; Lutz et al., 2021, 2022).”
BackgroundSelf-citationYang, Z., & Talha, M. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/1725490 · Cited by 38
“The term “collaborative education” refers to counselors and civics instructors overlapping, negotiating, increasing their strengths, and complementing their shortcomings in order to create the impact of a powerful combination [7].”
BackgroundRetractedA Framework to Guide Implementation of AI in Health Care: Protocol for a Cocreation Research Project
Nilsén, P., Svedberg, P., Neher, M., Nair, M., Larsson, I., Petersson, L., & Nygren, J. (2023) · JMIR Research Protocols · doi:10.2196/50216 · Cited by 37
“AI can also be used to predict disease progression and recommend treatment options based on a patient’s individual characteristics and medical history [7].”
Farmer, T. W., Serpell, Z. N., Scott, L. A., DeVlieger, S., Brooks, D. S., & Hamm, J. V. (2022) · Journal of Emotional and Behavioral Disorders · doi:10.1177/10634266211068892 · Cited by 33
“…subgroups, and communities, we need research approaches and corresponding intervention service delivery frameworks that promote the effective adaptation of strategies to the needs and circumstances of youth and contexts (Bickman, 2020; Chen et al., 2021; T. W. Farmer, Bierman, et al., 2021).”
BackgroundToward complementarity: Specificity and commonality in social-emotional development
Malti, T., Cheah, C. S. L., & Special Section Editors, S. A. C. S. G. I. S.-E. D. (2021) · Child Development · doi:10.1111/cdev.13690 · Cited by 33
Dong, F., Zhang, S., Zhu, J., & Sun, J. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18178946 · Cited by 28
AI Model for Blockchain Based Industrial Application in Healthcare IoT
Bibhu, V., Das, L., Rana, A., Sharma, S., & Salagrama, S. (2023) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-31952-5_8 · Cited by 25
Cross, S. P. M., Nicholas, J., Mangelsdorf, S. N., Valentine, L., Baker, S. T. E., McGorry, P. D., Gleeson, J., & Álvarez‐Jiménez, M. (2023) · JMIR Formative Research · doi:10.2196/49846 · Cited by 26
“After more than 50 years of randomized controlled trials focused on group-effect research, we simply do not know what works, for whom, and why [15].”
BackgroundA review on the efficacy of artificial intelligence for managing anxiety disorders
Das, K. P., & Gavade, P. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1435895 · Cited by 25
“Meanwhile, recent advancements indicate the emergence of AI-based engagement technologies, such as Natural Language Processing (NLP), which can be used to develop chatbots and intelligent agents to converse and engage patients using unstructured data like spoken language or text (Bickman, 2020).”
BackgroundInfluentialRetiring, Rethinking, and Reconstructing the Norm of Once-Weekly Psychotherapy
Schleider, J. L., Dobias, M., Mullarkey, M. C., & Ollendick, T. H. (2020) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01090-7 · Cited by 20
“Given sufficiently-large samples, artificial intelligence could help identify precision treatment “rules” indicating which treatment is most likely to benefit particular patients (Bickman 2020).”
BackgroundAI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients
Ćosić, K., Popović, S., Šarlija, M., Kesedžić, I., Gambiraža, M., Dropuljić, B., Mijić, I., Henigsberg, N., & Jovanović, T. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.782866 · Cited by 27
“A variety of digital technologies which may assist in this globally increasing problem, including AI-based tools, are being discussed in the literature (Bickman, 2020; Ćosić et al., 2020b; Maulik et al., 2020; Gargot et al., 2021).”
BackgroundInfluentialA machine learning algorithm for classification of mental tasks
Manoharan, H., Haleem, S. L. A., Selvarajan, S., Kshirsagar, P. R., Tirth, V., Thangamani, M., & Chandan, R. R. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107785 · Cited by 20
Nilsén, P., Reed, J. E., Nair, M., Savage, C., Macrae, C., Barlow, J. G., Svedberg, P., Larsson, I., Lundgren, L. E., & Nygren, J. (2022) · Frontiers in Health Services · doi:10.3389/frhs.2022.961475 · Cited by 23
GÜLTEKİN, M., & ŞAHİN, M. (2024) · Cyberpsychology Journal of Psychosocial Research on Cyberspace · doi:10.5817/cp2024-1-6 · Cited by 23
Sibley, M., Bickman, L., Atkins, D., Tanana, M. J., Coxe, S., Ortiz, M., Martin, P., King, J., Monroy, J. M., Ponce, T., et al. (2023) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2023.02.001 · Cited by 17
Self-citationWorking Towards the Development and Implementation of Precision Mental Healthcare: An Example
Lutz, W., Schwartz, B., Penedo, J. M. G., Boyle, K. S., & Deisenhofer, A. (2020) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01053-y · Cited by 19
“Applying AI may overcome the problem of low internal validity in observational data, allowing RCTs to be replaced by naturalistic trials and augmented by idiographic research (Bickman 2020).”
BackgroundSanchez, A. T., Park, A. L., Chu, W., Letamendi, A. M., Stanick, C., Regan, J., Perez, G., Manners, D., Oh, G., & Chorpita, B. F. (2021) · Journal of Community Psychology · doi:10.1002/jcop.22633 · Cited by 18
Leveraging Novel Technologies and Artificial Intelligence to Advance Practice-Oriented Research
Atzil–Slonim, D., Penedo, J. M. G., & Lutz, W. (2023) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-023-01309-3 · Cited by 23
“The use of classical statistical approaches in research to confirm or refute specific hypotheses is another set - back (Bickman, 2020 ).”
Ma, J. S., O’Riordan, M., Mazzer, K., Batterham, P. J., Bradford, S., Kõlves, K., Titov, N., Klein, B., & Rickwood, D. (2022) · JMIR Human Factors · doi:10.2196/34514 · Cited by 20
“AI has even been viewed by some academics as representing the future of mental health research methodology because of its superior ability to recognize the complexity of disorders, heterogeneity of clients, and varied mental health contexts compared with traditional statistical approaches that tend to rely on forecast…”
BackgroundPrecision Mental Health and Data-Informed Decision Support in Psychological Therapy: An Example
Lutz, W., Schaffrath, J., Eberhardt, S. T., Hehlmann, M. I., Schwartz, B., Deisenhofer, A., Vehlen, A., Schürmann, S., Uhl, J., & Moggia, D. (2023) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-023-01330-6 · Cited by 24
“…the Affective Style Questionnaire (ASQ; Hofmann & Kashdan, 2010) and the Assessment for Signal Clients (ASC; Lambert et al., 2007 ) are utilized every five ses - sions to cover five domains related to the therapy process: health concepts in medicine (e.g., Bickman, 2020; Lutz et al., 2022).”
BackgroundMethodPredicting the outcome of psychotherapy for chronic depression by person‐specific symptom networks
Schumacher, L., Klein, J. P., Hautzinger, M., Härter, M. C., Schramm, E., & Kriston, L. (2024) · World Psychiatry · doi:10.1002/wps.21241 · Cited by 19
“Individual differences in treatment efficacy and their sources are commonly disregarded in group comparison designs such as those of randomized controlled trials 6 .”
Integrating digital interventions with clinical practice in youth mental health services
Cross, S. P., Nicholas, J., Bell, I. H., Mangelsdorf, S., Valentine, L., Thompson, A., Gleeson, J. F., & Alvarez-Jimenez, M. (2023) · Australasian Psychiatry · doi:10.1177/10398562231169365 · Cited by 16
“20 From research to clinical practice: the implementation challenge Despite the mounting evidence for digital interventions and blended approaches in trials, examples of successful blended care integrated into mainstream youth mental health services are less common.”
BackgroundShih, C. W., Pudipeddi, R., Uthayakumar, A., & Washington, P. Y. (2021) · JMIRx Med · doi:10.2196/24972 · Cited by 15
“We envision the presented feasibility study of Quokka as the first step toward a local community-based precision health care approach [88-92] to mental health.”
MethodData Privacy, Ethics, and the Role of AI in Customer Relationship Management
Kumar, A., Sharma, A., Dhanka, S., Bist, Y., Maini, S., & Bhatnagar, P. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1867-7.ch013 · Cited by 15
Method Matters: Enhancing Voice‐Based Depression Detection With a New Data Collection Framework
Vilenchik, D., Cwikel, J., Ezra, Y., Hausdorff, T., Lazarov, M., Sergienko, R., Abramovitz, R., Schmidt, I., & Perez, A. S. (2025) · Depression and Anxiety · doi:10.1155/da/4839334 · Cited by 17
Horovitz, O. (2025) · Nutrients · doi:10.3390/nu17101744 · Cited by 22
“Future research will likely focus on optimizing digital interventions, exploring their long-term efficacy, and integrating them into traditional treatment frameworks [94].”
Hu, J. (2022) · Scientific Programming · doi:10.1155/2022/6357421 · Cited by 21
Liu, H. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.902456 · Cited by 16
“AI advancements are also helping the behavioral and mental health care industries (5).”
BackgroundMethodArtificial Intelligence and Mental Health Issues: A Narrative Review
Salcedo, Z. B. V., Tari, I. D. A. E. P. D., Ratsameemonthon, C., & Setiyani, R. Y. (2023) · Journal of Public Health Sciences · doi:10.56741/jphs.v2i02.282 · Cited by 16
“By utilizing large amounts of patient data, AI algorithms can learn to recognize the complex and nuanced features of mental health disorders and make more accurate diagnoses [4].”
BackgroundUsing AI-Supported Supervision in a University Telemental Health Training Clinic
Sheperis, D., & Sadeh‐Sharvit, S. (2023) · Journal of Technology in Counselor Education and Supervision · doi:10.61888/2692-4129.1093 · Cited by 16
Adams, J. (2024) · Digital Society · doi:10.1007/s44206-024-00110-5 · Cited by 17
“Cutting across these three categories, artificial intelligence (AI) can be used to enhance treatment options using internet-based, app-based or VR methods or to enable potentially more precise diagnostic and monitoring tools (Bickman, 2020).”
Peretz, G., Taylor, C. B., Ruzek, J. I., Jefroykin, S., & Sadeh‐Sharvit, S. (2023) · JMIR Formative Research · doi:10.2196/45156 · Cited by 14
“Studies could also assess whether a homework assignment that had been discussed was further reviewed in the following therapy appointment, whether homework was completed, and whether treatment review and homework completion are associated with improved clinical outcomes [32].”
BackgroundEditorial: Transforming Youth Mental Health Treatment Through Digital Technology
Gleeson, J., Riper, H. M., & Álvarez‐Jiménez, M. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.606433 · Cited by 14
“Bickman foreshadowed an increasing roll for precisionmental health by leveraging big data (1).”
BackgroundInfluentialMental health progress requires causal diagnostic nosology and scalable causal discovery
Saxe, G., Bickman, L., Ma, S., & Aliferis, C. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.898789 · Cited by 13
Self-citationDesigning personalized mental health interventions for anxiety: CBT therapists’ perspective
Balaskas, A., Schueller, S. M., Doherty, K., Cox, A. L., & Doherty, G. J. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103319 · Cited by 15
“Through AI-based technologies (e.g., machine learning, deep learning), it is possible to inform the selection and delivery of treatment components (Bickman, 2020; Thieme et al., 2020).”
Artificial Intelligence’s Contribution To Mental Health Education
Anand, N., Pant, L. M., Alam, T., Pundir, S., Thomas, L., & Rakshith, U. R. (2023) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds56580.2023.10104905 · Cited by 13
“different processes to improve them [3].”
Sridhar, A., Olesegun, O., & Drahota, A. (2023) · Global Implementation Research and Applications · doi:10.1007/s43477-023-00086-3 · Cited by 12
“However, in usual care or community-based child MH-care settings, MH-EBIs are provided with less frequency and intensity than is recommended for effectiveness, and interventions may not be delivered with sufficient fidelity to yield positive child outcomes (Bickman, 2020; Garland et al., 2013).”
BackgroundExplainable machine learning methods to predict postpartum depression risk
Shivaprasad, S., Chadaga, K., Sampathila, N., Prabhu, S., Chadaga, R., & Swathi, K. S. (2024) · Systems Science & Control Engineering · doi:10.1080/21642583.2024.2427033 · Cited by 11
“Therefore, in addition to improving illness models, AI/ML may help psychiatry by identifying at-risk individuals, customizing care, and identifying pharmaceutical treatments (Bickman, 2020; Ćosić et al., 2020).”
Optimized Active Fuzzy Deep Federated Learning for predicting autism spectrum disorder
Daliri, A., Khalilian, M., Mohammadzadeh, J., & Hosseini, S. S. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00523-3 · Cited by 11
“The main problem is to measure the accuracy of prediction methods (Bickman 2020).”
Cross, S. P. M., Mangelsdorf, S. N., Valentine, L., O’Sullivan, S., McEnery, C., Scott, I., Gilbertson, T., Louis, S., Myer, J., Liu, P., et al. (2025) · Internet Interventions · doi:10.1016/j.invent.2025.100849 · Cited by 16
“AI and machine learning further enhance this by detecting patterns, predicting outcomes, and tailoring interventions in real time (Bickman, 2020).”
InfluentialA Roadmap to an Equitable Digital Diabetes Ecosystem
Kerr, D., Klonoff, D. C., Bergenstal, R. M., Choudhary, P., & Ji, L. (2023) · Endocrine Practice · doi:10.1016/j.eprac.2022.12.016 · Cited by 10
Ooi, P. B., & Wilkinson, G. G. (2024) · British Journal of Guidance and Counselling · doi:10.1080/03069885.2024.2373180 · Cited by 13
“Some researchers believe AI will form the basis of a radical transformation or a new era in mental health care with the potential for precision or personalised mental health services and interventions (e.g. Alwadi & Lathifa, 2022; Bickman, 2020; Zhou et al., 2022).”
Gallo, C. G., Berkel, C., Mauricio, A. M., Sandler, I. N., Wolchik, S. A., Villamar, J. A., Mehrotra, S., & Brown, C. H. (2021) · Families Systems & Health · doi:10.1037/fsh0000590 · Cited by 10
“SSI facilitates the collection of data through low burden, automated, or unobtrusive measures (Webb et al., 1999) involving machine coding of digitized information (Gallo, Pantin, et al., 2015) and analyses from machine learning algorithms (Bickman, 2020; Brown, 2020).”
BackgroundMethodThe Potential of Wearable Sensors for Detecting Cognitive Rumination: A Scoping Review
Arnold, V. X., & Young, S. D. (2025) · Sensors · doi:10.3390/s25030654 · Cited by 11
“That said, their implementation must be judicious to avoid questionnaire fatigue and ensure sustained user engagement [45,95].”
Ofem, U. J., Anake, P. M., Abuo, C. B., Ukatu, J. O., & Etta, E. O. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1414178 · Cited by 9
“Bickman (45) reported that most of the AI tools, like the Chatbot, offer various opportunities for interacting with clients and obtaining information that can be used by the counsellor to identify areas of need and develop intervention programmes that can aid in suggesting solutions and treatment…”
Kumar, A., Sharma, A., Bhatnagar, P., Dhanka, S., & Bist, Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1867-7.ch004 · Cited by 9
Yeasmin, S., Semi, M. M. A., Rony, M. K. K., Das, S., Sabeena, A. A., Rahman, R., Biswas, B., Ahmed, F., & Hossain, A. (2025) · Health Science Reports · doi:10.1002/hsr2.71703 · Cited by 18
“These diverse applications indicate that AI can support both early diagnosis and long‐term mental health tracking across a range of populations [26–28].”
Norris, M. L. (2024) · European Eating Disorders Review · doi:10.1002/erv.3114 · Cited by 10
Translational Informatics for Natural Products as Antidepressant Agents
Singla, R. K., Joon, S., Shen, L., & Shen, B. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.738838 · Cited by 9
“…struggling with issues of privacy and con fi dentiality of patient information amongst myriad other challenges (Grist et al., 2018; Graham et al., 2019; Hategan et al., 2019; Huckvale et al., 2019; Romano and Tatonetti, 2019; Tran et al., 2019; Bickman, 2020; Ke et al., 2020; Mennen et al., 2021).”
BackgroundArtificial Intelligence and Predictive Modeling in Mental Health
Hilty, D. M., Cheng, Y., & Luxton, D. D. (2024) · Springer eBooks · doi:10.1007/978-3-031-59936-1_13 · Cited by 12
Sun, Q. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/5230215 · Cited by 8
“It has become necessary to develop a new educational paradigm as a result of the widespread use of the Internet and the creation of instructional media [15].”
BackgroundRetractedAI-Driven Tools for Detecting and Monitoring Mental Health Conditions Though Behaviour Patterns
Kasaudhan, K. (2025) · International Journal of Preventive Medicine and Health · doi:10.54105/ijpmh.b1054.05030325 · Cited by 11
Potential Benefits of Remote Continuous Care for Depression
Forman‐Hoffman, V. L., Kvedar, J. C., & Ranta, K. (2021) · International Journal of Digital Health · doi:10.29337/ijdh.39 · Cited by 7
Evaluation of Health Personnel's Thoughts on Artificial Intelligence
ORHAN, M., & Bülez, A. (2022) · Kesit Akademi · doi:10.29228/kesit.66404 · Cited by 7
An End-to-End framework for extracting observable cues of depression from diary recordings
Mlakar, I., Ariöz, U., Smrke, U., Plohl, N., Šafran, V., & Rojc, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125025 · Cited by 7
“Artificial Intelligence (AI) and Machine Learning (ML) represent a promising pathway towards timely and accurate screening and diagnosis of depression 28–30 .”
BackgroundChatbots for the Detection of Covid-19: A Systematic Review of the Literature
Albites-Tapia, A., Gamboa-Cruzado, J., Almeyda-Ortiz, J., & Lázaro, A. M. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.01304113 · Cited by 9
“Health [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [17] [18[19] [20] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [35] [36] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [53] [54] [55] [56] [57] [58] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72…”
Using machine learning to develop a five-item short form of the children’s depression inventory
Lin, S., Wang, C., Jiang, X., Zhang, Q., Luo, D., Li, J., Xu, J., & Xu, J. (2024) · BMC Public Health · doi:10.1186/s12889-024-18657-w · Cited by 8
“Some scholars point out that ML can help mental health practitioners define mental illness more objectively, contribute to the early identification of illness and effective intervention, and promote the development of psychiatric and psychological disciplines [28, 29].”
BackgroundDeng, Z., Tian, Z., Xue, J., & Gupta, S. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2272191 · Cited by 6
“Research has established that past experiences in fl uences people ’ s behaviour (Bickman 2020; Nel and Bosho ff 2019).”
Influentialde Almeida, R. S., & da Silva, T. P. (2021) · Advances in psychology, mental health, and behavioral studies (APMHBS) book series · doi:10.4018/978-1-7998-8634-1.ch011 · Cited by 6
The utilization of artificial intelligence in life science research and healthcare delivery
Shah, H., Jayabalan, B., & Mery, A. (2023) · Salud Ciencia y Tecnología · doi:10.56294/saludcyt2023450 · Cited by 6
The 100 most influential papers in medical artificial intelligence; a bibliometric analysis
Zahoor, F., Abdullah, M., Tahir, M. W., & Islam, A. M. (2024) · Journal of the Pakistan Medical Association · doi:10.47391/jpma.10064 · Cited by 8
AI Integration in Behavioral Healthcare: A Practical Framework for Clinicians
Sadeh‐Sharvit, S., & Hollon, S. D. (2025) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-025-00532-z · Cited by 8
Obeid, N., Lavallée, N., Bradley, A. H. M., & Norris, M. L. (2025) · Journal of Eating Disorders · doi:10.1186/s40337-025-01351-6 · Cited by 5
“It offers a future where treat-ment is not only evidence-based, but tailored to each person’s biology, environment, and lived experience [22, 26, 35].”
Advanced data analysis and prediction model for student mental health risk assessment
Shi, X., Pan, J., Yuan, D., Li, M., & Pan, Y. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1682083 · Cited by 10
“However, most existing methods primarily focus on single-modal data, such as speech recognition, facial expression analysis, and social media text analysis, which have limitations in terms of dynamicity and personalized prediction (Bickman, 2020).”
Thommandru, A., & Mone, V. (2026) · Journal of Responsible Technology · doi:10.1016/j.jrt.2026.100159 · Cited by 5
Meyer, J. K. V., de Rozas, A. B. O., Muñoz, I., Burmeister, C., Macchiavello, C., Lendzion, L., & Garcia‐Huidobro, D. (2023) · Health Promotion International · doi:10.1093/heapro/daad004 · Cited by 5
Distributional moderation analysis: Unpacking moderation effects in intervention research
Wiedermann, W., Reinke, W. M., & Herman, K. C. (2024) · Journal of School Psychology · doi:10.1016/j.jsp.2024.101399 · Cited by 5
Chong, M. K., Hickie, I. B., Ekambareshwar, M., Poulsen, A., McKenna, S., Hutcheon, A., Hockey, S. J., Scott, E. M., & Iorfino, F. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12889-1 · Cited by 5
“Further, studies show that machine learning models integrated into digital MBC tools can improve diagnosis and prognosis of clients’ illness trajectories based on collected within-individual data [16, 17].”
Liu, H., & Chen, X. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/7307741 · Cited by 5
“, dm}: Since the dataset D includes the containing subset D, each set D1 has C; values in it [15].”
BackgroundEvolution of Telehealth in Ambulatory Psychiatry: A One Year Perspective
Peck, P., Torous, J. B., & Sullivan, S. (2021) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-021-01148-0 · Cited by 5
“As mental health services continue to adopt, new technologies will likely continue to accelerate change (Bickman 2020).”
BackgroundSingh, P., Singh, G., Singh, A., & Singh, J. (2022) · Cybernetics & Systems · doi:10.1080/01969722.2022.2122009 · Cited by 5
“In recent times, deep learning is mostly applicable to application issues like mental illness detections (Bickman 2020), traffic accident and traffic flow risk predictions, and stock market predictions (Wu et al. 2020).”
BackgroundPAJER, K. A., Honeywell, C. R., Howley, H. E. A., Sheridan, N., Affleck, W., Terekhov, I., & Radhakrishnan, D. (2024) · Frontiers in Health Services · doi:10.3389/frhs.2024.1405426 · Cited by 5
“Precision health, which aims to tailor care to the individual ’ s biological (e.g., genomic, metabolic, or neuroimaging biomarkers), lifestyle, psychological, and environmental characteristics holds great promise as a transformational paradigm (11 – 13).”
BackgroundArtificial Intelligence and Psychotherapy
Bal, F. (2025) · Psikiyatride Guncel Yaklasimlar - Current Approaches in Psychiatry · doi:10.18863/pgy.1538432 · Cited by 6
Manevich, A., & Aluma, S. (2025) · OMEGA - Journal of Death and Dying · doi:10.1177/00302228251372418 · Cited by 4
Ahuna, J. K., & Becker, K. D. (2024) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434241303806 · Cited by 5
“NDM studies focused on technological design may direct their efforts towards electronic health records and arti fi cial intelligence, which are becoming increasingly widespread in mental health (Bickman, 2020; Kariotis et al., 2022).”
Yousif, N., Youssef, E., & Gad, S. (2024) · European Journal of Education · doi:10.1111/ejed.12889 · Cited by 10
Effectiveness of XR‐Based Exposure Therapy for Phobic Disorders
Lamb, R. L., Perry, J., Sutherland, E. C., Hoston, D., Garris, A., & DeRiggs, A. (2025) · Journal of Counseling & Development · doi:10.1002/jcad.12557 · Cited by 4
Kaló, Z., Imre, A., Tatár, M., & Nagy, B. (2025) · Psychiatry Research · doi:10.1016/j.psychres.2025.116634 · Cited by 4
“Bickman outlines five key challenges in mental health services where AI can have a transformative impact: i) diagnoses muddle, ii) poorly designed measures, iii) primacy of randomized clinical trials, iv) lack of learning through feedback and, v) insufficiency of treatment precision (Bickman, 2020).”
Ready or not? Psychologists’ perceptions of work readiness in the age of AI
McDonald, C., & Schweinsberg, A. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1524024 · Cited by 7
Leveraging generative AI to simulate mental healthcare access and utilization
VanHook, C., Abusuampeh, D. K., & Pollard, J. F. (2025) · Frontiers in Health Services · doi:10.3389/frhs.2025.1654106 · Cited by 5
Automated segmentation of child-clinician speech in naturalistic clinical contexts
Bertamini, G., Furlanello, C., Chétouani, M., Cohen, D., & Venuti, P. (2025) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2024.104906 · Cited by 4
Cross, S. P. M., Liu, P., Scott, I., O’Sullivan, S., Nicholas, J., Valentine, L., Mangelsdorf, S. N., Baker, S. T. E., Gleeson, J., & Álvarez‐Jiménez, M. (2025) · Behaviour Research and Therapy · doi:10.1016/j.brat.2025.104703 · Cited by 5
Understanding GenAI Adoption Among Undergraduate Learners: Perceptions, Use, and Familiarity
Ketsman, O., & Lazarević, B. (2026) · Journal of Educational Technology Systems · doi:10.1177/00472395261416160 · Cited by 6
“As GenAI-driven technologies become more integrated into higher education, their potential to support student learning, provide personalized instruction (Kaswan et al., 2023), and facilitate research (Bickman, 2020) is recognized.”
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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