Real concerns, artificial intelligence: Reality testing for psychiatrists
Bibliographic record
Abstract
The use of augmented or artificial intelligence (AI) in healthcare promises groundbreaking advancements, from increasing diagnostic accuracy and minimizing clinical errors to personalized treatment plans and automated clinical decision-making. Its use may allow us to transition from phenomenological categories of psychiatric illness to one driven by underlying etiology and realize the Research Domain Criteria (RDoC) model proposed by the (U.S.) National Institutes of Mental Health (NIMH), which today remains difficult to apply clinically and is accessible primarily to researchers. AI may facilitate the transition to a more syncretic framework of understanding psychiatric illness that accounts for disruptions, all the way from the cellular level to the level of social systems. Yet, despite immense possibilities, there are also associated risks. In this article, we explore the challenges and opportunities associated with the use of AI in psychiatry, focusing on the potential ethical and health equity considerations in vulnerable populations, especially in child and adolescent psychiatry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".