The Evolution From Standardized to Virtual Patients in Medical Education
Bibliographic record
Abstract
Standardized patients (SPs) are widely used in medical education to teach clinical skills and provide assessments. SPs allow students to practice history taking, physical exams, and communication in controlled settings. However, SPs have limitations such as fatigue, performance variability, and the inability to simulate certain conditions, which virtual patients (VPs) can address. VPs can address these limitations and offer consistency, scalability, and adaptability. Although VPs are being implemented in research settings, they have the potential to be powerful medical education tools. Advancements in immersive technologies such as virtual reality, haptic feedback, and artificial intelligence (AI) will allow the creation of hyper-realistic, interactive training environments that mimic the complexity of real patient encounters. Medical students will be able to engage with VPs in fully immersive settings, complete with haptic feedback and AI-driven dialogue, allowing for more lifelike diagnostic and procedural experiences. The wider availability of such technologies through web services has implications for global medical education and assessment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".