ReviewCureus2026
Artificial Intelligence and Psychiatric Training: Opportunities, Challenges, and the Future of Mental Health Education.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is rapidly transforming healthcare delivery, research, and medical education. Within psychiatry, advances in machine learning, natural language processing, large language models (LLMs), and AI-enabled simulation are reshaping clinical practice and trainee education. Educational and clinical applications include personalized learning, virtual patient encounters, clinical decision support, longitudinal electronic health record (EHR) synthesis, automated feedback, and documentation assistance. These tools may strengthen diagnostic reasoning, psychopharmacology education, psychotherapy training, and administrative efficiency while allowing more time for patient-facing care. However, algorithmic bias, misinformation, privacy, academic integrity, cost, and overreliance may undermine independent clinical judgment. These risks are especially important for novice learners and in child and adolescent assessment, where chronological age does not capture cognitive development, cultural context, or nonverbal and affective cues. Empirical outcome data evaluating formal AI curriculum implementation in psychiatry residency and fellowship programs remain limited, with few studies assessing effects on trainee competence, clinical reasoning, error recognition, patient safety, or sustained clinical performance. This narrative review examines current applications, limitations, and ethical considerations and proposes a staged, competency-based curriculum in which trainees establish core interviewing, observation, and diagnostic skills before progressing to supervised clinical AI use. AI should augment, rather than replace, human interpretation, therapeutic relationships, and professional accountability. Accordingly, this review is primarily directed toward graduate medical education leaders, psychiatry residency and fellowship program directors, and clinical educators who are responsible for the development, implementation, and oversight of competency-based AI curricula within psychiatric training programs.
Indexed as
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.