Diagnostic Accuracy, Implementation Barriers, and Equity Implications of Teledermatology in Rural Skin Cancer: Scoping Review
Bibliographic record
Abstract
Background: Skin cancer is the most commonly diagnosed malignancy in the United States, with rural populations facing disproportionate delays in diagnosis due to geographic isolation, workforce shortages, and limited access to dermatologic care. These delays contribute to higher rates of late-stage diagnosis and poorer outcomes. Teledermatology has emerged as a promising solution to expand access to dermatologic evaluation and treatment in underserved settings. Objective: The review aims to evaluate the diagnostic performance, implementation challenges, and equity considerations of teledermatology in the context of rural skin cancer care, and to assess its potential to improve clinical outcomes in underserved populations. Methods: A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar to identify studies published between January 2015 and March 2025. Search terms included "teledermatology," "skin cancer," "rural health services," "telemedicine," "diagnostic accuracy," and "health disparities." Studies evaluating diagnostic metrics, time to diagnosis, patient satisfaction, and implementation barriers were included. Results: Nine key studies spanning various countries and health care settings were included. Diagnostic sensitivity ranged from 41.9% to 100%, and specificity from 46% to 90%, depending on modality and lesion type. Teledermatology consistently reduced time to diagnosis, in some cases by over 75%, and was associated with high patient satisfaction due to increased convenience and reduced travel. Key barriers included technological limitations, inconsistent imaging protocols, and reimbursement variability. Successful implementation was facilitated by standardized workflows, dermoscopy integration, and centralized platforms. Conclusions: Teledermatology is a viable and effective approach to addressing disparities in rural skin cancer care. It offers diagnostic accuracy comparable to face-to-face evaluations while reducing wait times and improving patient satisfaction. Overcoming technological and systemic barriers is critical to ensuring equitable, long-term integration of teledermatology in rural health systems.
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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 | 0.017 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".