Are we ready to integrate modern technologies into the medical curriculum for students a systematic review
Notice bibliographique
Résumé
This study aims to explore the diverse applications of contemporary technological innovations in education and to propose effective strategies for their integration into the curriculum, addressing the complexities and collaborative efforts required for meaningful learning experiences. This systematic review examines the integration of digital health tools, virtual reality, and artificial intelligence (AI) in medical education, adhering to PRISMA guidelines and Cochrane Handbook standards. The primary research question focuses on the benefits and challenges of incorporating these technologies into the medical curriculum. A comprehensive literature search from 2010 to September 2024 was conducted across Scopus, Web of Science, Embase, PubMed, and IEEE Xplore databases, selecting 24 relevant studies out of 3842 for thematic analysis, revealing seven key themes. The study utilized Rayyan for screening and consensus-building, followed the PRISMA Checklist for data extraction, and conducted quantitative and qualitative analyses, with stakeholder consultation for future research. The study shows that medical students and faculty are generally ready to incorporate modern technologies into their curricula, but many lack a basic understanding of their applications in medicine. It emphasizes the need for a comprehensive redesign of educational frameworks to effectively incorporate modern technologies such as AI, virtual reality (VR), and augmented reality (AR). Research demonstrates that these technologies enhance learning outcomes, improve students' understanding of complex medical concepts, and develop critical skills. The review emphasizes the transformative potential of simulation-based technologies, which can significantly boost confidence, teamwork, and communication skills among medical students. However, successful integration requires careful planning of curriculum topics based on technological capabilities. Contemporary technologies could be integrated into medical education, offering personalized learning, improved patient care, and practical training. However, technical hurdles, financial constraints, and ethical considerations must be addressed. This transition will provide long-term cost-effectiveness and enhance the value of education. Medical educators have praised the use of innovative technologies as valuable learning tools. However, the concepts of utilisation and integration should not be confused. The educational system remains heavily reliant on teacher-centered and human-centric models, with concerns about the extent of teachers' ability to provide education and the validity of education across generations. Policymakers collaborating with accreditation bodies can help deliver uniform education that caters to students' learning preferences, but teachers may lack the capabilities and resources to lead this transformation. This raises questions about whether teachers consciously employ technology to reduce their significance and whether increased satisfaction with modern education may reflect a decline in teachers' role.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,023 | 0,133 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,015 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».