Dermoscopy Education in Undergraduate Medical Education: Scoping Review of Pedagogical Approaches and Gaps
Bibliographic record
Abstract
Purpose A dermatoscope, a hand-held magnifying polarized illuminating device, is a powerful tool in the clinician's tool kit for skin cancer examination (SCE), allowing for better screening and diagnostic accuracy. Yet, few physicians are trained to leverage its potential. This scoping review explores current approaches to dermoscopy education among undergraduate medical trainees. A systematic scoping review was conducted by searching 5 databases: Medline, Embase + Classic, Scopus, Web of Science, and Education Resources Information Center. Major Findings A total of 12 primary articles met the inclusion criteria published between 2012 and 2024 with a total of 1286 participating medical students, most of whom were clerks (n studies = 6, 50.0%, n participants = 498/1286 = 38.7%). Most studies were pre- and posttest control trials (n S = 5, 41.7%, n P = 980/1286 = 76.2%) assessing SCE performance after dermoscopy teaching. Short-term retention and performance were evaluated in 9 studies (n S = 9, 75.0%, n P = 884/1286, 68.7%), in which 5 studies reported immediate statistically significant improvement in posttest scores following their respective educational intervention ( P < .05) (n S = 5, 41.7%, n P = 323/1286 = 25.1%). Long-term retention was assessed in 3 studies with heterogenous findings (n S = 3, 25.0%, n P = 432/1286 = 33.6%). Many studies lack explicit reference to a structured framework for teaching dermoscopy (n S = 7, 58.3%, n P = 849/1286 = 66.0%). Conclusions The literature has shown successful curricular implementation and effectiveness of dermoscopy teaching at this level. Future research may focus on strategies for curricular integration, long-term retention, and connections to later stages of training and patient care.
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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 | 0.039 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".