ArticleJMIR formative research2021
Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.
Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT04061642 (A Feasibility Study of a Hybrid-Classic/Deep-Learning Enabled Clinical Decision Aid for Personalized and Individualized Pharmacological Depression Treatment Selection), which is not on this map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Feasibility Study of a Hybrid-Classic/Deep-Learning Enabled Clinical Decision Aid for Personalized and Individualized Pharmacological Depression Treatment Selection
Who cites it
10 citing papers in PubMed, 38 citations in OpenAlex.
- Clinicians' Attitudes and Perceptions on the Adoption of AI in Mental Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- Bridging algorithmic prediction and clinical agency: an exploratory pilot study of AI-augmented physician antidepressant choice.Frontiers in psychiatry · 2026Article
- The Use of Artificial Intelligence for Personalized Treatment in Psychiatry.Current psychiatry reports · 2025Review
- Development of the treatment prediction model in the artificial intelligence in depression - medication enhancement study.Npj mental health research · 2025Article
- The Use of AI in Mental Health Services to Support Decision-Making: Scoping Review.Journal of medical Internet research · 2025Article
- Benefits of Clinical Decision Support Systems for the Management of Noncommunicable Chronic Diseases: Targeted Literature Review.Interactive journal of medical research · 2024Review
- Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets.Translational psychiatry · 2024Article
- Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings.Yearbook of medical informatics · 2023Article
- Barriers and solutions to the adoption of translational tools for computational psychiatry.Molecular psychiatry · 2023Review
- A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study.Frontiers in psychiatry · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
31 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundApproximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.
objectiveThis study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction.
methodsOwing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews.
resultsData were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F
conclusionsOur findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies.
trial registrationClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.
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