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.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".