From promise to practice: towards the realisation of AI-informed mental health care
Open accessAbstract
In this Series paper, we explore the promises and challenges of artificial intelligence (AI)-based precision medicine tools in mental health care from clinical, ethical, and regulatory perspectives. The real-world implementation of these tools is increasingly considered the prime solution for key issues in mental health, such as delayed, inaccurate, and inefficient care delivery. Similarly, machine-learning-based empirical strategies are becoming commonplace in psychiatric research because of their potential to adequately deconstruct the biopsychosocial complexity of mental health disorders, and hence to improve nosology of prognostic and preventive paradigms. However, the implementation steps needed to translate these promises into practice are currently hampered by multiple interacting challenges. These obstructions range from the current technology-distant state of clinical practice, over the lack of valid real-world databases required to feed data-intensive AI algorithms, to model development and validation considerations being disconnected from the core principles of clinical utility and ethical acceptability. In this Series paper, we provide recommendations on how these challenges could be addressed from an interdisciplinary perspective to pave the way towards a framework for mental health care, leveraging the combined strengths of human intelligence and AI.
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
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?
There exists the risk that LLMs perpetuate inequities and stigma, further widening mental health disparities.
“There exists the risk that LLMs perpetuate inequities and stigma, further widening mental health disparities [47].”
From The Opportunities and Risks of Large Language Models in Mental Health (2024) · cites this paper as [47]
The absence of ethical considerations in WoeBot and Wysa becomes evident in their responses to emergencies such as immediate harm or suicidal ideation, where they lack clinical grounding and contextual awareness.
“The absence of ethical considerations in WoeBot and Wysa becomes evident in their responses to emergencies such as immediate harm or suicidal ideation, where they lack clinical grounding and contextual awareness (Koutsouleris et al., 2022).”
From A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement (2023) · cites this paper as Koutsouleris et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · see citing works
Toward the future of psychiatric diagnosis: the seven pillars of RDoC
Cuthbert, B. N., & Insel, T. R. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-126 · Cited by 3,053 · see citing works
Principles of mixed-initiative user interfaces
Horvitz, E. (1999) · Proceedings of the SIGCHI conference on Human factors in computing systems the CHI is the limit - CHI '99 · doi:10.1145/302979.303030 · Cited by 1,490 · see citing works
Social determinants of mental health
Allen, J., Balfour, R., Bell, R., & Marmot, M. (2014) · International Review of Psychiatry · doi:10.3109/09540261.2014.928270 · Cited by 1,411 · see citing works
Do no harm: a roadmap for responsible machine learning for health care
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0548-6 · Cited by 1,266 · see citing works
Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations
Chen, J. H., & Asch, S. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmp1702071 · Cited by 1,171 · see citing works
Machine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047 · see citing works
Computational psychiatry as a bridge from neuroscience to clinical applications
Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4238 · Cited by 1,002 · see citing works
Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970 · see citing works
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966 · see citing works
Self-citationFiske, A., Henningsen, P., & Buyx, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13216 · Cited by 857 · see citing works
Are Racial and Ethnic Minorities Less Willing to Participate in Health Research?
Wendler, D. S., Kington, R. S., Madans, J. H., Wye, G. V., Christ‐Schmidt, H., Pratt, L. A., Brawley, O. W., Gross, C. P., & Emanuel, E. J. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0030019 · Cited by 855 · see citing works
Machine Learning for Precision Psychiatry: Opportunities and Challenges
Bzdok, D., & Meyer‐Lindenberg, A. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.11.007 · Cited by 833 · see citing works
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians
Asan, O., Bayrak, A. E., & Choudhury, A. (2020) · Journal of Medical Internet Research · doi:10.2196/15154 · Cited by 825 · see citing works
Cross-trial prediction of treatment outcome in depression: a machine learning approach
Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00471-x · Cited by 676 · see citing works
Ethical Machine Learning in Healthcare
Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-114757 · Cited by 645 · see citing works
Predicting consumer behavior with web search
Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1005962107 · Cited by 636 · see citing works
Implementing Measurement-Based Care in Behavioral Health: A Review
Lewis, C. C., Boyd, M., Puspitasari, A., Navarro, E., Howard, J., Kassab, H., Hoffman, M., Scott, K., Lyon, A., Douglas, S., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.3329 · Cited by 605 · see citing works
A Tipping Point for Measurement-Based Care
Fortney, J. C., Unützer, J., Wrenn, G. L., Pyne, J. M., Smith, G. R., Schoenbaum, M., & Harbin, H. T. (2016) · Psychiatric Services · doi:10.1176/appi.ps.201500439 · Cited by 568 · see citing works
Methods in predictive techniques for mental health status on social media: a critical review
Chancellor, S., & De Choudhury, M. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0233-7 · Cited by 557 · see citing works
Artificial intelligence and algorithmic bias: implications for health systems
Panch, T., Mattie, H., & Atun, R. (2019) · Journal of Global Health · doi:10.7189/jogh.09.020318 · Cited by 533 · see citing works
The promise of machine learning in predicting treatment outcomes in psychiatry
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., et al. (2021) · World Psychiatry · doi:10.1002/wps.20882 · Cited by 531 · see citing works
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Mireille Hildebrandt, C. C., & del Castillo, C. C. (2020) · doi:10.1145/3351095 · Cited by 486 · see citing works
Lee, E., Torous, J. B., Choudhury, M. D., Depp, C. A., Graham, S. A., Kim, H., Paulus, M. P., Krystal, J. H., & Jeste, D. V. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.02.001 · Cited by 477 · see citing works
Using Measurement-Based Care to Enhance Any Treatment
Scott, K., & Lewis, C. C. (2015) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2014.01.010 · Cited by 461 · see citing works
The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes
Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.03.009 · Cited by 443 · see citing works
D’Alfonso, S. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.04.005 · Cited by 408 · see citing works
Agniel, D., Kohane, I. S., & Weber, G. M. (2018) · BMJ · doi:10.1136/bmj.k1479 · Cited by 391 · see citing works
Deep learning in mental health outcome research: a scoping review
Su, C., Xu, Z., Pathak, J., & Wang, F. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0780-3 · Cited by 368 · see citing works
Torous, J., & Roberts, L. W. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0262 · Cited by 362 · see citing works
Strohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P. C., & Moors, E. H. M. (2020) · European Radiology · doi:10.1007/s00330-020-06946-y · Cited by 329 · see citing works
Barnett, P., Mackay, E., Matthews, H., Gate, R., Greenwood, H., Ariyo, K., Bhui, K. S., Halvorsrud, K., Pilling, S., & Smith, S. M. (2019) · The Lancet Psychiatry · doi:10.1016/s2215-0366(19)30027-6 · Cited by 311 · see citing works
CHI Conference on Human Factors in Computing Systems
Barbosa, S. D. J., Lampe, C. A. C., Appert, C., Shamma, D. A., Drucker, S. M., Williamson, J., & Yatani, K. (2022) · doi:10.1145/3491102 · Cited by 309 · see citing works
Popel, M., Tomková, M., Tomek, J., Kaiser, Ł., Uszkoreit, J., Bojar, O., & Žabokrtský, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-18073-9 · Cited by 302 · see citing works
Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse
Verheij, R. A., Ćurčin, V., Delaney, B. C., & McGilchrist, M. M. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9134 · Cited by 293 · see citing works
Koutsouleris, N., Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., Popovic, D., Oeztuerk, O., Haas, S. S., Weiske, J., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.3604 · Cited by 275 · see citing works
Self-citationKretzschmar, K., Tyroll, H., Pavarini, G., Manzini, A., Singh, I., & NeurOx Young People’s Advisory Group (2019) · Biomedical Informatics Insights · doi:10.1177/1178222619829083 · Cited by 270 · see citing works
Use of Digital Mental Health for Marginalized and Underserved Populations
Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019) · Current Treatment Options in Psychiatry · doi:10.1007/s40501-019-00181-z · Cited by 268 · see citing works
Beckmann, J. S., & Lew, D. (2016) · Genome Medicine · doi:10.1186/s13073-016-0388-7 · Cited by 261 · see citing works
Beware explanations from AI in health care
Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021) · Science · doi:10.1126/science.abg1834 · Cited by 252 · see citing works
State of the Field of Mental Health Apps
Neary, M., & Schueller, S. M. (2018) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2018.01.002 · Cited by 247 · see citing works
Machine translation systems and quality assessment: a systematic review
Rivera-Trigueros, I. (2021) · Language Resources and Evaluation · doi:10.1007/s10579-021-09537-5 · Cited by 239 · see citing works
Review of Machine Learning Algorithms for Diagnosing Mental Illness
Cho, G., Yim, J., Choi, Y., Ko, J., & Lee, S. H. (2019) · Psychiatry Investigation · doi:10.30773/pi.2018.12.21.2 · Cited by 237 · see citing works
Projected workforce of psychiatrists in the United States: a population analysis
Satiani, A., Niedermier, J., Satiani, B., & Svendsen, D. P. (2018) · Psychiatric Services · doi:10.1176/appi.ps.201700344 · Cited by 235 · see citing works
Treating health disparities with artificial intelligence
Chen, I. Y., Joshi, S., & Ghassemi, M. (2020) · Nature Medicine · doi:10.1038/s41591-019-0649-2 · Cited by 235 · see citing works
Kambeitz, J. P., Cabral, C., Sacchet, M. D., Gotlib, I. H., Zahn, R., Serpa, M. H., Walter, M., Falkai, P., & Koutsouleris, N. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.10.028 · Cited by 233 · see citing works
Self-citationTime to reality check the promises of machine learning-powered precision medicine.
Wilkinson, J., Arnold, K. F., Murray, E. J., van Smeden, M., Carr, K., Sippy, R., de Kamps, M., Beam, A., Konigorski, S., Lippert, C., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30200-4 · Cited by 233 · see citing works
de Pablo, G. S., Studerus, E., Vaquerizo‐Serrano, J., Irving, J., Catalán, A., Oliver, D., Baldwin, H., Danese, A., Fazel, S., Steyerberg, E. W., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa120 · Cited by 221 · see citing works
Kambeitz, J. P., Kambeitz‐Ilankovic, L., Leucht, S., Wood, S. J., Davatzikos, C. A., Malchow, B., Falkai, P., & Koutsouleris, N. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.22 · Cited by 217 · see citing works
Self-citationA Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media
Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M. B., & De Choudhury, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287587 · Cited by 214 · see citing works
The Science of Prognosis in Psychiatry: A Review
Fusar-Poli, P., Hijazi, Z., Stahl, D., & Steyerberg, E. W. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.2530 · Cited by 197 · see citing works
Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health
D'Alfonso, S., Santesteban-Echarri, O., Rice, S., Wadley, G., Lederman, R., Miles, C., Gleeson, J., & Alvarez-Jimenez, M. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00796 · Cited by 192 · see citing works
A Path for Translation of Machine Learning Products into Healthcare Delivery
(2020) · EMJ Innovations · doi:10.33590/emjinnov/19-00172 · Cited by 172 · see citing works
Hauser, T. U., Iannaccone, R., Ball, J., Mathys, C., Brandeis, D., Walitza, S., & Brem, S. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1093 · Cited by 168 · see citing works
Self-citationComputational neuroimaging strategies for single patient predictions
Stephan, K. E., Schlagenhauf, F., Huys, Q. J. M., Raman, S., Aponte, E. A., Brodersen, K. H., Rigoux, L., Moran, R., Jean Daunizeau, A., Dolan, R. J., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.038 · Cited by 166 · see citing works
Trifan, A., Oliveira, M., & Oliveira, J. L. (2019) · JMIR mhealth and uhealth · doi:10.2196/12649 · Cited by 163 · see citing works
The Role of Digital Navigators in Promoting Clinical Care and Technology Integration into Practice
Wisniewski, H., Gorrindo, T., Rauseo-Ricupero, N., Hilty, D. M., & Torous, J. B. (2020) · Digital Biomarkers · doi:10.1159/000510144 · Cited by 157 · see citing works
Toward a More-Than-Human Analysis of Digital Health: Inspirations From Feminist New Materialism
Lupton, D. A. (2019) · Qualitative Health Research · doi:10.1177/1049732319833368 · Cited by 150 · see citing works
Lee, M. K., & Rich, K. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445570 · Cited by 145 · see citing works
Schoeler, T., Petros, N., Forti, M. D., Klamerus, E., Foglia, E., Murray, R. M., & Bhattacharyya, S. (2017) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30233-x · Cited by 138 · see citing works
Explainable AI for Healthcare: From Black Box to Interpretable Models
Adadi, A., & Berrada, M. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-981-15-0947-6_31 · Cited by 137 · see citing works
Toward a revised nosology for attention-deficit/hyperactivity disorder heterogeneity
Nigg, J. T., Karalunas, S. L., Feczko, E., & Fair, D. A. (2020) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.02.005 · Cited by 132 · see citing works
Technology-Based Mental Health Treatment and the Impact on the Therapeutic Alliance
Lopez, A., Schwenk, S., Schneck, C. D., Griffin, R. J., & Mishkind, M. C. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1055-7 · Cited by 128 · see citing works
Oh, J., Makar, M., Fusco, C., McCaffrey, R., Rao, K. K., Ryan, E. E., Washer, L., West, L. R., Young, V. B., Guttag, J. V., et al. (2018) · Infection Control and Hospital Epidemiology · doi:10.1017/ice.2018.16 · Cited by 124 · see citing works
Torous, J., Staples, P., Barnett, I., Sandoval, L. R., Keshavan, M., & Onnela, J.-P. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0022-8 · Cited by 123 · see citing works
From Treatment to Healing:Envisioning a Decolonial Digital Mental Health
Pendse, S. R., Nkemelu, D., Bidwell, N. J., Jadhav, S., Pathare, S., De Choudhury, M., & Kumar, N. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501982 · Cited by 115 · see citing works
Self-citationSystematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses
Benoit, J. R. A., Onyeaka, H., Keshavan, M. S., & Torous, J. B. (2020) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000268 · Cited by 114 · see citing works
Wei, W.-Q., Leibson, C. L., Ransom, J. E., Kho, A. N., Caraballo, P. J., Chai, H. S., Yawn, B. P., Pacheco, J. A., & Chute, C. G. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000597 · Cited by 112 · see citing works
‘AI gone mental’: engagement and ethics in data-driven technology for mental health
Carr, S. (2020) · Journal of Mental Health · doi:10.1080/09638237.2020.1714011 · Cited by 107 · see citing works
Computer-assisted Therapy in Psychiatry: Be Brave—It’s a New World
Carroll, K. M., & Rounsaville, B. J. (2010) · Current Psychiatry Reports · doi:10.1007/s11920-010-0146-2 · Cited by 105 · see citing works
Svensson, B., & Hansson, L. I. (2015) · Nordic Journal of Psychiatry · doi:10.3109/08039488.2015.1109140 · Cited by 104 · see citing works
Kim, Y., & Na, K. (2017) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.06.024 · Cited by 102 · see citing works
Birnbaum, M., Ernala, S. K., Rizvi, A., Arenare, E., Meter, A. V., de Choudhury, M., & Kane, J. M. (2019) · npj Schizophrenia · doi:10.1038/s41537-019-0085-9 · Cited by 96 · see citing works
Ricard, B. J., Marsch, L. A., Crosier, B. S., & Hassanpour, S. (2018) · Journal of Medical Internet Research · doi:10.2196/11817 · Cited by 95 · see citing works
Identifying and mitigating biases in EHR laboratory tests
Pivovarov, R., Albers, D. J., Sepulveda, J. L., & Elhadad, N. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.03.016 · Cited by 91 · see citing works
Digital health data-driven approaches to understand human behavior
Marsch, L. A. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0761-5 · Cited by 90 · see citing works
The promise of a model-based psychiatry: building computational models of mental ill health
Hauser, T. U., Skvortsova, V., Choudhury, M. D., & Koutsouleris, N. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00152-2 · Cited by 89 · see citing works
Self-citationArtificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges
Ienca, M., & Ignatiadis, K. (2020) · AJOB Neuroscience · doi:10.1080/21507740.2020.1740352 · Cited by 84 · see citing works
Mohr, D. C., Shilton, K., & Hotopf, M. H. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0251-5 · Cited by 82 · see citing works
Artificial Intelligence in Psychiatry
Fakhoury, M. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-981-32-9721-0_6 · Cited by 77 · see citing works
Cross-Cultural Differences in the Use of Online Mental Health Support Forums
Pendse, S. R., Niederhoffer, K. G., & Sharma, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359169 · Cited by 60 · see citing works
Raket, L. L., Jaskolowski, J., Kinon, B. J., Brasen, J. C., Jönsson, L., Wehnert, A., & Fusar-Poli, P. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30024-8 · Cited by 60 · see citing works
Pendse, S. R., Sharma, A., Vashistha, A., Choudhury, M. D., & Kumar, N. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445410 · Cited by 59 · see citing works
Four equity considerations for the use of artificial intelligence in public health
Smith, M. J., Axler, R. E., Bean, S., Rudzicz, F., & Shaw, J. (2020) · Bulletin of the World Health Organization · doi:10.2471/blt.19.237503 · Cited by 52 · see citing works
Richter, T., Fishbain, B., Richter-Levin, G., & Okon-Singer, H. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11100957 · Cited by 52 · see citing works
Integrating Predictive Modeling Into Mental Health Care: An Example in Suicide Prevention
Reger, G. M., McClure, M. L., Ruskin, D., Carter, S. P., & Reger, M. A. (2018) · Psychiatric Services · doi:10.1176/appi.ps.201800242 · Cited by 50 · see citing works
Cohen, A. S., Schwartz, E. K., Le, T. P., et al. (2020) · World Psychiatry · doi:10.1002/wps.20703 · Cited by 50 · see citing works
Roemmich, K., & Andalibi, N. (2021) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3476049 · Cited by 50 · see citing works
Annual Research Review: Developmental computational psychiatry
Hauser, T. U., Will, G., Dubois, M., & Dolan, R. J. (2018) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12964 · Cited by 49 · see citing works
Self-citationChien, W. T., Mui, J., Gray, R., & Cheung, E. (2016) · BMC Psychiatry · doi:10.1186/s12888-016-0744-6 · Cited by 48 · see citing works
Thompson, L. K., Sugg, M. M., & Runkle, J. D. (2018) · Social Science & Medicine · doi:10.1016/j.socscimed.2018.08.025 · Cited by 48 · see citing works
Will artificial intelligence eventually replace psychiatrists?
Brown, C., Story, G. W., Mourão-Miranda, J., & Baker, J. T. (2019) · The British Journal of Psychiatry · doi:10.1192/bjp.2019.245 · Cited by 43 · see citing works
Beyond Googling: The Ethics of Using Patients' Electronic Footprints in Psychiatric Practice
Fisher, C., & Appelbaum, P. (2017) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000145 · Cited by 40 · see citing works
Bradstreet, S., Allan, S., & Gumley, A. I. (2019) · Journal of Mental Health · doi:10.1080/09638237.2019.1630727 · Cited by 39 · see citing works
Embedded Systems and Artificial Intelligence
Bhateja, V., Satapathy, S. C., & Satori, H. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-0947-6 · Cited by 39 · see citing works
Integrating sleep, neuroimaging, and computational approaches for precision psychiatry
Goldstein‐Piekarski, A. N., Holt-Gosselin, B., O’Hora, K., & Williams, L. M. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0483-8 · Cited by 38 · see citing works
Rauseo-Ricupero, N., Henson, P., Agate-Mays, M., & Torous, J. B. (2021) · International Review of Psychiatry · doi:10.1080/09540261.2020.1859465 · Cited by 38 · see citing works
Heuer, H., Jarke, J., & Breiter, A. (2021) · Big Data & Society · doi:10.1177/20539517211017593 · Cited by 38 · see citing works
Precision Medicine Versus Evidence-Based Medicine
Blackstone, E. H. (2019) · Circulation · doi:10.1161/circulationaha.119.043014 · Cited by 36 · see citing works
Cheah, P. Y., Jatupornpimol, N., Hanboonkunupakarn, B., Khirikoekkong, N., Jittamala, P., Pukrittayakamee, S., Day, N., Parker, M. J., & Bull, S. (2018) · BMC Medical Ethics · doi:10.1186/s12910-018-0326-x · Cited by 33 · see citing works
Rodríguez, F., Scheinker, D., & Harrington, R. A. (2018) · Circulation Research · doi:10.1161/circresaha.118.314119 · Cited by 33 · see citing works
Generating value with mental health apps
Powell, A. C., Torous, J. B., Firth, J., & Kaufman, K. R. (2020) · BJPsych Open · doi:10.1192/bjo.2019.98 · Cited by 33 · see citing works
Chilman, N., Song, X., Roberts, A., Tolani, E., Stewart, R. J., Chui, Z., Birnie, K., Harber-Aschan, L., Gazard, B., Chandran, D., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-042274 · Cited by 31 · see citing works
Gilbert, J. R., Symmonds, M., Hanna, M. G., Dolan, R. J., Friston, K. J., & Moran, R. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.057 · Cited by 30 · see citing works
Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments
Lin, E., Lin, C., & Lane, H. (2021) · Clinical Psychopharmacology and Neuroscience · doi:10.9758/cpn.2021.19.4.577 · Cited by 29 · see citing works
Kim, S., & Lee, K. (2021) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s339412 · Cited by 25 · see citing works
Constructing Epidemiologic Cohorts from Electronic Health Record Data
Williams, B. A. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182413193 · Cited by 21 · see citing works
Murphy, J., Michalak, E. E., Liu, J., Colquhoun, H. L., Burton, H., Yang, X., Yang, T., Wang, X., Fei, Y., He, Y., et al. (2021) · BMC Psychiatry · doi:10.1186/s12888-021-03442-5 · Cited by 20 · see citing works
Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders
Hänsel, K., Lin, I. W., Sobolev, M., Muscat, W., Yum-Chan, S., Choudhury, M. D., Kane, J. M., & Birnbaum, M. L. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.691327 · Cited by 18 · see citing works
Building SuperModels: emerging patient avatars for use in precision and systems medicine
Brown, S.-A. N. (2015) · Frontiers in Physiology · doi:10.3389/fphys.2015.00318 · Cited by 17 · see citing works
Hansen, L., Enevoldsen, K., Bernstorff, M., Nielbo, K. L., Danielsen, A. A., & Østergaard, S. D. (2021) · Acta Neuropsychiatrica · doi:10.1017/neu.2021.22 · Cited by 17 · see citing works
Yang, J., Soltan, A. A. S., Yang, Y., & Clifton, D. A. (2022) · medRxiv · doi:10.1101/2022.01.13.22268948 · Cited by 14 · see citing works
Pattern classification as decision support tool in antipsychotic treatment algorithms
Korda, A. I., Andreou, C., & Borgwardt, S. (2021) · Experimental Neurology · doi:10.1016/j.expneurol.2021.113635 · Cited by 13 · see citing works
Big-Data-Ansätze in der Psychiatrie: Beispiele aus der Depressionsforschung
Bzdok, D., Karrer, T. M., Habel, U., & Schneider, F. W. (2017) · Der Nervenarzt · doi:10.1007/s00115-017-0456-2 · Cited by 11 · see citing works
Clarke, T., & Barwick, M. A. (2021) · Child and Adolescent Mental Health · doi:10.1111/camh.12447 · Cited by 9 · see citing works
Pre-trained language models to extract information from radiological reports.
Úbeda, P. L., Díaz–Galiano, M. C., López, L. A. U., et al. (2021) · CLEF (Working Notes) · Cited by 5 · see citing works
Patient journey through cases of depression from claims database using machine learning algorithms
Kitanishi, Y., Fujiwara, M., & Binkowitz, B. S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247059 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 9
- 24 Bzdok D, Karrer TM, Habel U, Schneider F. Big data approaches in psychiatry: examples in depression research. Nervenarzt 2018; 89: 869-74 (in German).
- 43 D'Alfonso S. AI in mental health. Curr Opin Psychol 2020; 36: 112-17 .
- 48 Aulamo M, Sulubacak U, Virpioja S, Tiedemann J. OpusTools and parallel corpus diagnostics. Proceedings of the 12th Language Resources and Evaluation Conference. France: European Language Resources Association, 2020. 3782-89.
- 69 Wei Q, Ji Z, Si Y, et al. Relation extraction from clinical narratives using pre-trained language models. AMIA Annual Symposium Proceedings. American Medical Informatics Association, 2019: 1236-45.
- 84 Pawar U, O'Shea D, Rea S, O'Reilly R. Explainable AI in healthcare. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). Dublin, Ireland. IEEE 2020: 1-2.
- 117 Bartoletti I. AI in healthcare: ethical and privacy challenges. Conference on Artificial Intelligence in Medicine in Europe. Singapore: Springer, 2019: 7-10.
- 122 Committee on the Learning Health Care System in America, Institute of Medicine. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington: National Academies Press, 2013. http://www.ncbi.nlm.nih.gov/ books/NBK207225/ (accessed May 14, 2022).
- 124 Horvitz EJ. Reflections on challenges and promises of mixedinitiative interaction. AI Mag 2007; 28: 19-22.
- 125 Todd PM, Gigerenzer G. What is ecological rationality? Ecological Rationality. Oxford: Oxford University Press, 2012.
Cited by
Sorted by citations
Li, H., Zhang, R., Lee, Y.-C., Kraut, R. E., & Mohr, D. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00979-5 · Cited by 598 · see citing works
“Powered by NLP, machine learning and deep learning, these AI-based CAs possess expanding capabilities to process more complex information and thus allow for more personalized, adaptive, and sophisticated responses to mental health needs 8,9 .”
The Opportunities and Risks of Large Language Models in Mental Health
Lawrence, H. R., Schneider, R. A., Rubin, S. B., Matarić, M. J., McDuff, D. J., & Jones Bell, M. (2024) · JMIR Mental Health · doi:10.2196/59479 · Cited by 235 · see citing works
Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot
Habicht, J., Viswanathan, S., Carrington, B., Hauser, T. U., Harper, R., & Rollwage, M. (2024) · Nature Medicine · doi:10.1038/s41591-023-02766-x · Cited by 132 · see citing works
Self-citationCorona Hernández, H., Corcoran, C., Achim, A. M., de Boer, J. N., Boerma, T., Brederoo, S. G., Cecchi, G. A., Ciampelli, S., Elvevåg, B., Fusaroli, R., et al. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbac215 · Cited by 56 · see citing works
Smith, A., Hachen, S., Schleifer, R., Bhugra, D., Buadze, A., & Liebrenz, M. (2023) · International Journal of Social Psychiatry · doi:10.1177/00207640231178451 · Cited by 57 · see citing works
Bashkirova, A., & Krpan, D. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100066 · Cited by 55 · see citing works
Sarkar, S., Gaur, M., Chen, L. K., Garg, M., & Srivastava, B. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1229805 · Cited by 38 · see citing works
Levin, C., Naimi, E., & Saban, M. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105566 · Cited by 17 · see citing works
The opportunities and risks of large language models in mental health
Lawrence, H. R., Schneider, R. A., Rubin, S. B., Mataric, M. J., McDuff, D. J., & Bell, M. J. (2024) · arXiv · doi:10.48550/arxiv.2403.14814
- Authors
- Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M.
- Affiliations
- King's College London (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Koutsouleris, Nikolaos
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 4
- Issue
- 11
- Pages
- e829-e840
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W4304112197
- Cited by
- 251
- References
- 118
- Citations by year
- 2026: 58 · 2025: 95 · 2024: 59 · 2023: 37 · 2022: 2
- FWCI
- 3.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Science Foundation (2027689, 1816403)
- Emory University
- National Alliance for Research on Schizophrenia and Depression (27023)
- Wellcome Trust (211155, 211155/Z/18/Z)
- Leverhulme Trust
- Bundesministerium für Bildung und Forschung
- Jacobs Foundation (2017-1261-04, 27023)
- National Institutes of Health
- Centers for Disease Control and Prevention
- Injury Prevention Research Center
- National Institute of Mental Health (R01MH117172)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in mental health care, precision medicine, machine learning, implementation barriers, clinical utility, moral acceptability, regulatory considerations, real-world data, model validation, biopsychosocial model, human-AI collaboration
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Mental Disorders, Humans, Mental Health
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
