Information and Communication Technologies (ICT) in Medical Education and Practice: The Major Challenges
Bibliographic record
Abstract
This literature review addresses the main effects and challenges in using information and communication technologies (ICT) in medical education and practice. The first challenge is to better prepare future physicians for the changing behaviours of patients, who are increasingly Internet-savvy and who sometimes appear to know more about their diseases than their physicians. The second challenge, which is closely linked to the first, is to raise awareness among physicians in training of the many benefits of using ICT to improve not only the quality of interventions and health care delivery but, from a broader perspective, the organization of the health care system itself. The third challenge is to motivate medical students and practitioners to use ICT to find information, learn and develop. It is proposed that information literacy should be a mandatory skill for all medical students. The e-learning mode of training is also addressed. Although underemployed in most medical faculties, it represents the future of initial and continuous medical training. Virtual resources and communities, simulations and 3D animations are also discussed. The fourth and final challenge is to change medical teaching practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".