Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models
Background: We evaluated whether a large language model could assist in selecting psychopharmacological treatments for adults with treatment-resistant depression. Methods: We generated 20 clinical vignettes reflecting treatment-resistant depression among adults based on distributions drawn from elec...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Journal of Mood and Anxiety Disorders |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950004425000392 |
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| author | Roy H. Perlis Pilar F. Verhaak Joseph Goldberg Cristina Cusin Michael Ostacher Gin S. Malhi Carlos A. Zarate Richard C. Shelton Dan V. Iosifescu Mauricio Tohen Manish Kumar Jha Martha Sajatovic Michael Berk |
| author_facet | Roy H. Perlis Pilar F. Verhaak Joseph Goldberg Cristina Cusin Michael Ostacher Gin S. Malhi Carlos A. Zarate Richard C. Shelton Dan V. Iosifescu Mauricio Tohen Manish Kumar Jha Martha Sajatovic Michael Berk |
| author_sort | Roy H. Perlis |
| collection | DOAJ |
| description | Background: We evaluated whether a large language model could assist in selecting psychopharmacological treatments for adults with treatment-resistant depression. Methods: We generated 20 clinical vignettes reflecting treatment-resistant depression among adults based on distributions drawn from electronic health records. Each vignette was evaluated by 2 expert psychopharmacologists to determine and rank the 5 best next-step pharmacologic interventions, as well as contraindicated or poor next-step treatments. Vignettes were then presented in random order, permuting gender and race, to a large language model (Qwen 2.5:7B), augmented with a synopsis of published treatment guidelines. Model output was compared to expert rankings, as well as to those of a convenience sample of community clinicians and an additional group of expert clinicians. Results: The augmented model prioritized the expert-designated optimal choice for 114/320 vignettes (35.6 %, 95 % CI 30.6 %–41.0 %; Cohen’s kappa = 0.34, 95 % CI 0.28–0.39). There were no vignettes for which any of the model choices were among the poor or contraindicated treatments. Results were not meaningfully different when gender or race of the vignette was permuted to examine risk for bias. A sample of community clinicians identified the optimal treatment choice for 12/91 vignettes (13.2 %, 95 % CI: 7.7–21.6 %; Cohen’s kappa = 0.10, 95 % CI 0.03–0.18), while an additional group of expert psychopharmacologists identified optimal treatment for 9/140 (6.4 %, 95 %CI: 3.4–11.8 %; Cohen’s kappa = 0.03, 95 % CI 0.01–0.08). Conclusion: An augmented language model demonstrated moderate agreement with expert recommendations and avoided contraindicated treatments, suggesting potential as a tool for supporting complex psychopharmacologic decision-making in treatment-resistant depression. |
| format | Article |
| id | doaj-art-41c99ac2b45749858c1b0fa87c8fb535 |
| institution | Kabale University |
| issn | 2950-0044 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Mood and Anxiety Disorders |
| spelling | doaj-art-41c99ac2b45749858c1b0fa87c8fb5352025-08-20T03:51:19ZengElsevierJournal of Mood and Anxiety Disorders2950-00442025-12-011210014210.1016/j.xjmad.2025.100142Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language modelsRoy H. Perlis0Pilar F. Verhaak1Joseph Goldberg2Cristina Cusin3Michael Ostacher4Gin S. Malhi5Carlos A. Zarate6Richard C. Shelton7Dan V. Iosifescu8Mauricio Tohen9Manish Kumar Jha10Martha Sajatovic11Michael Berk12Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, Boston, MA, United States; Correspondence to: Massachusetts General Hospital, 185 Cambridge Street, 6th Floor, Boston, MA 02114, United States.Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, Boston, MA, United StatesDepartment of Psychiatry, Mt. Sinai School of Medicine, New York, NY, United StatesDepartment of Psychiatry, Harvard Medical School, Boston, MA, United StatesDepartment of Psychiatry, Stanford University School of Medicine, Stanford, CA, United StatesAcademic Department of Psychiatry, Kolling Institute, Northern Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; CADE Clinic and Mood-T, Royal North Shore Hospital, Northern Sydney Local Health District, St. Leonards, NSW, Australia; Department of Psychiatry, and Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy,University of Oxford, Oxford, UKSection on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United StatesDepartment of Psychiatry and Behavioral Neurobiology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United StatesNYU School of Medicine and Nathan Kline Institute, NY, United StatesDepartment of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, United StatesDepartment of Psychiatry, UT Southwestern Medical Center, Dallas, TX, United StatesUniversity Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH, United StatesSchool of Medicine, Deakin University and Barwon Health, AustraliaBackground: We evaluated whether a large language model could assist in selecting psychopharmacological treatments for adults with treatment-resistant depression. Methods: We generated 20 clinical vignettes reflecting treatment-resistant depression among adults based on distributions drawn from electronic health records. Each vignette was evaluated by 2 expert psychopharmacologists to determine and rank the 5 best next-step pharmacologic interventions, as well as contraindicated or poor next-step treatments. Vignettes were then presented in random order, permuting gender and race, to a large language model (Qwen 2.5:7B), augmented with a synopsis of published treatment guidelines. Model output was compared to expert rankings, as well as to those of a convenience sample of community clinicians and an additional group of expert clinicians. Results: The augmented model prioritized the expert-designated optimal choice for 114/320 vignettes (35.6 %, 95 % CI 30.6 %–41.0 %; Cohen’s kappa = 0.34, 95 % CI 0.28–0.39). There were no vignettes for which any of the model choices were among the poor or contraindicated treatments. Results were not meaningfully different when gender or race of the vignette was permuted to examine risk for bias. A sample of community clinicians identified the optimal treatment choice for 12/91 vignettes (13.2 %, 95 % CI: 7.7–21.6 %; Cohen’s kappa = 0.10, 95 % CI 0.03–0.18), while an additional group of expert psychopharmacologists identified optimal treatment for 9/140 (6.4 %, 95 %CI: 3.4–11.8 %; Cohen’s kappa = 0.03, 95 % CI 0.01–0.08). Conclusion: An augmented language model demonstrated moderate agreement with expert recommendations and avoided contraindicated treatments, suggesting potential as a tool for supporting complex psychopharmacologic decision-making in treatment-resistant depression.http://www.sciencedirect.com/science/article/pii/S2950004425000392Major depressionArtificial intelligenceMachine learningPsychopharmacologyExpert consensus |
| spellingShingle | Roy H. Perlis Pilar F. Verhaak Joseph Goldberg Cristina Cusin Michael Ostacher Gin S. Malhi Carlos A. Zarate Richard C. Shelton Dan V. Iosifescu Mauricio Tohen Manish Kumar Jha Martha Sajatovic Michael Berk Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models Journal of Mood and Anxiety Disorders Major depression Artificial intelligence Machine learning Psychopharmacology Expert consensus |
| title | Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models |
| title_full | Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models |
| title_fullStr | Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models |
| title_full_unstemmed | Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models |
| title_short | Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models |
| title_sort | clinical decision support for pharmacologic management of treatment resistant depression with augmented large language models |
| topic | Major depression Artificial intelligence Machine learning Psychopharmacology Expert consensus |
| url | http://www.sciencedirect.com/science/article/pii/S2950004425000392 |
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