ArticleNpj mental health research2025
Development of the treatment prediction model in the artificial intelligence in depression - medication enhancement study.
Article in Npj mental health research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.Pharmacopsychiatry · 2026Review
- Artificial intelligence-based positive youth development intervention protocol in school settings.Frontiers in psychology · 2026Article
- Disrupted synapses: prefrontal cortex-reward circuit dysfunction in stress-induced depression-like behaviors.Frontiers in cellular neuroscience · 2026Review
- 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
- Toward precision psychological rehabilitation: predicting CBT efficacy in post-stroke depression using machine learning.Frontiers in psychiatry · 2025Article
- Precision medicine decision-making under pharmacogenomics in nursing: realizing value from limited evidence to scaled clinical pathways.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce an artificial intelligence model to personalize treatment in major depression, which was deployed in the Artificial Intelligence in Depression: Medication Enhancement Study. We predict probabilities of remission across multiple pharmacological treatments, validate model predictions, and examine them for biases. Data from 9042 adults with moderate to severe major depression from antidepressant clinical trials were used to train a deep learning model. On the held-out test-set, the model demonstrated an AUC of 0.65, outperformed a null model (p = 0.01). The model increased population remission rate in hypothetical and actual improvement testing. While the model identified escitalopram as generally outperforming other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. The model did not amplify potentially harmful biases. We demonstrate the first model capable of predicting outcomes for 10 treatments, intended to be used at or near the start of treatment to personalize treatment selection.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.