Are we ready to integrate modern technologies into the medical curriculum for students a systematic review
Bibliographic record
Abstract
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.
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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 | 0.023 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".