The Effectiveness of The Application of Artificial Intelligence in Teaching Students of Medical Institutions
Bibliographic record
Abstract
The purpose of this article is to study the effectiveness of integrating artificial intelligence into medical education and to identify the key advantages, challenges, and prospects. A comparative analysis of the implementation of artificial intelligence technologies in Kyrgyzstan, Kazakhstan, Tajikistan, Canada, Germany, Austria, and Switzerland was carried out. Randomised controlled trials, meta-analyses, systematic reviews, and surveys were used to assess the effectiveness of introducing artificial intelligence into medical university curricula. It was found that the use of artificial intelligence technologies helps to reduce task completion time by an average of 35 minutes, improve clinical skills (effect 0.68; 95% CI: 0.30-1.06, p<0.001) and increase satisfaction with the learning process (effect 0.46; 95% CI: 0.26-0.66, p<0.001). The level of knowledge about artificial intelligence was studied, which was 0.4 (95% CI: 0.3-0.5, p<0.01), and the positive attitude of students towards its use was 0.7 (95% CI: 0.6-0.8, p<0.01). It has been shown that adaptive learning systems based on artificial intelligence improve diagnostic skills, accuracy of clinical decisions, and situation modelling. Virtual and augmented reality contribute to the safe development of skills. The challenges of implementing technologies are identified, including the need for ethical standards, minimising algorithmic biases, and preparing teachers.
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 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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".