The DOI matches its Crossref record; 355 of the 356 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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].”
BackgroundInfluential
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
