Bibliographic record
Abstract
Introduction The incorporation of artificial intelligence (AI) in healthcare, especially in mental health services, offers potential advancements in efficiency and personalization. As AI technologies like machine learning and natural language processing (NLP) continue to evolve, it’s vital to evaluate their applications in psychiatry comprehensively. Objectives This review aims to summarize and characterize studies that used AI, particularly machine learning and NLP, in mental health. Additionally, it endeavors to understand how these technologies may enhance diagnostic tools, symptom monitoring, and delivery of personalized treatment in psychiatry. Methods Adhering to PRISMA guidelines, a systematic search was executed across multiple medical databases, including PubMed, Scopus, ScienceDirect, and PsycINFO. Keywords encompassed machine learning, data mining, psychiatry, and mental health. Exclusion criteria included non-English papers, anonymization process descriptions, case studies, conference papers, and other reviews. Data from various segments in the provided information were synthesized to capture the broader picture of AI’s application in psychiatry. Results From the 327 articles initially identified, 58 were chosen for detailed review. Studies predominantly revolved around three main populations: patients in medical databases, emergency room visitors, and social media users. The primary applications of AI entailed symptom extraction, illness severity classification, therapy effectiveness comparison, and psychopathological insights derivation. Data sources mainly included medical records and social media, with Python emerging as the preferred platform for most studies. Conclusions While AI shows immense promise in revolutionizing mental health care, its current applications largely confirm existing clinical hypotheses. Ethical concerns, such as patient privacy and data biases, remain paramount. Future work should delve deeper into these challenges while further exploring AI’s potential in clinical psychiatry practice. Disclosure of Interest None Declared
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".