Artificial Intelligence in Medical and Psychological Education: A Scoping Review and Suggested Curriculum for Medical Students (Preprint)
Notice bibliographique
Résumé
BACKGROUND Artificial intelligence (AI) is revolutionizing healthcare, significantly enhancing diagnostic accuracy, clinical decision-making, and operational efficiency. However, the pace of AI integration into medical education has lagged behind, leaving students inadequately prepared for the emerging challenges AI brings to healthcare. Key issues such as AI’s ethical implications, transparency, and inherent biases remain critical concerns that need to be addressed. While there is growing support for AI’s role in medical practice, many medical curricula still lack structured AI training programs, limiting students’ ability to fully leverage AI’s potential. OBJECTIVE This paper reviews the current state of AI education programs in medical and psychology training and proposes a model AI curriculum that can be used as a case example to illustrate how AI can be integrated effectively into medical education. METHODS A scoping review was conducted following the PRISMA-ScR guidelines to analyze existing AI education programs. Searches were performed in PubMed, PsycINFO, and Web of Science (2008–2023) for relevant studies. The inclusion criteria focused on programs designed for medical and psychological professionals. Data were extracted, synthesized narratively, and visualized. Screening was performed using Rayyan, and disagreements were resolved by reviewers. RESULTS From 5,364 records, 20 relevant programs were identified. The majority of programs (50%) were from the United States, with others coming from Canada, Germany, France, China, and the Netherlands. Topics covered included foundational AI concepts, programming, ethical concerns, governance, and AI’s role in clinical decision-making. Most programs were extracurricular (60%), and evaluation results highlighted that while technical skills were often taught, many programs lacked in-depth practical applications or hands-on experience with AI tools. Ethical and governance topics were also a common focus. In light of these findings, we propose five principles for a successful curriculum with a strong psychiatric perspective in order to both improve skills on AI and increase the attractiveness of psychiatry among medical students. CONCLUSIONS The integration of AI into medical and psychology curricula is essential for producing well-rounded healthcare professionals. To prepare students for AI’s role in healthcare, educational programs should be mandatory and focus on foundational AI knowledge, ethical considerations, data privacy, and clinical decision-making. These programs should align with the WHO’s guiding principles, ensuring that topics such as Explainable AI, Natural Language Processing (NLP), and algorithmic biases are comprehensively covered. Furthermore, it is crucial to foster collaboration with universities in low- and middle-income countries (LMICs) to ensure equitable access to AI education, bridging global disparities in healthcare technology. Such efforts will contribute to the sustainable, inclusive growth of AI in healthcare, enabling all healthcare systems to benefit from advancements in AI technologies.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,106 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,017 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».