BT01 The effectiveness of telemedicine in dermatology services: a systematic review
Bibliographic record
Abstract
Abstract Teledermatology (TD), the remote diagnosis and management of skin conditions using digital platforms, has rapidly evolved since its initial adoption in 1995. The COVID-19 pandemic accelerated the use of TD, particularly in regions with limited access to dermatologists, offering a viable alternative to face-to-face consultations. As TD continues to evolve, understanding its clinical utility, patient outcomes and economic impact becomes increasingly important. This systematic review evaluates the effectiveness of TD across three key domains: clinical outcomes, patient satisfaction and cost-effectiveness. It aims to provide a comprehensive assessment of the utility of TD as an alternative to traditional in-person dermatology services, with a focus on diagnostic accuracy, patient experiences and financial implications. A systematic search was conducted across MEDLINE, Embase and Web of Science for studies published between 2010 and July 2024. Studies comparing TD with face-to-face consultations in terms of diagnostic accuracy, patient satisfaction and cost-effectiveness were included. The quality of the included studies was assessed using the Newcastle–Ottawa Scale. Of 2768 articles identified, 23 studies met the inclusion criteria. Clinical outcomes indicated moderate agreement between TD and face-to-face consultations, with a mean kappa coefficient of 0.57, reflecting diagnostic concordance. High-resolution imaging was found to significantly improve diagnostic accuracy, particularly in asynchronous TD services. Notably, the mean kappa value for asynchronous TD was higher than that for synchronous TD (0.71 vs. 0.56), highlighting the importance of image quality. Additionally, TD demonstrated substantial cost savings, averaging USD 81.31 per patient, with savings ranging from 6.27% to 45.3%, depending on the healthcare system. TD also showed significant operational efficiencies, reducing overhead costs and improving appointment scheduling, especially in rural and underserved areas. Patient satisfaction varied widely, with 26.6% of patients willing to replace face-to-face consultations with TD. Satisfaction was notably influenced by the quality of the technical infrastructure and the availability of support during consultations. Older patients and those with lower digital literacy reported more difficulties, which reduced their willingness to adopt TD. TD offers moderate diagnostic accuracy, significant cost savings and varying degrees of patient acceptance. High-resolution imaging, clinician training and robust technical infrastructure are critical for optimizing diagnostic performance and patient satisfaction with TD. While TD can be an effective tool, particularly for minor or nonurgent dermatological conditions, it should complement, rather than replace, in-person consultations for more complex cases. Further research is needed to refine the role of TD in dermatology, exploring ways to integrate it effectively into healthcare systems and ensure its equitable accessibility.
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".