The Practice of Teledermatology Before, During, and After the COVID-19 Pandemic
Bibliographic record
Abstract
Background Since the beginning of the COVID-19 pandemic, the use of telemedicine has quickly expanded in many countries as clinical frameworks have been forced to shift to virtual platforms to guarantee the safety of patients and staff. Teledermatology, specifically, is well-suited for telemedicine, with evidence supporting its viability, even-handed quality and precision, and cost adequacy in comparison to in-person visits. Teledermatology holds extraordinary potential for expanding access to patients and guaranteeing coherence of care, especially for those from rural and underserved regions. Objective The aim of this research is to study the practice of teledermatology before, during, and after the COVID-19 pandemic. Methods A literature search using the following keywords was done: online consultations, teledermatology, post-covid. Reports from integrated health care companies (eg, Practo) were also considered. Results According to the reports, Indians consulted physicians 10 times more during the second wave (April to May 2021) of the pandemic than in pre–COVID-19 times (January to February 2020). India experienced a record 30-fold spike in web-based physician consultations for COVID-19–related symptoms during this time, as compared to a 6-fold increase during the previous peak. More than 50% of all web-based consultations were for pulmonologists and general physicians for queries related to COVID-19 and the seasonal flu. Other key specialties that were consulted during this period included gynecology (10%), dermatology (8%), and pediatrics (5%). The demand for general physicians and pulmonologists was at an all-time high, according to the data. Cutaneous manifestations were varied, and included urticaria, varicella-like vesicles, transient livedoid eruptions, livedoid vasculopathy, purpuric eruptions, lichenoid photodermatitis, erythroderma, photocontact dermatitis, and generalized pustular figurate erythema. Conclusions Continued advocacy efforts and future studies highlighting teledermatology’s impact, particularly on minorities, underserved patient populations, and in resource-poor settings, are critical for long-term legislative changes to occur and to provide coverage to our most vulnerable patients. This presentation underscores the state of teledermatology prior to the pandemic, the legal statutory changes that permitted teledermatology to rapidly expand during the pandemic, and the significance of continued work after the pandemic. In short, the interruption of everyday life worldwide caused by SARS-CoV-2 has demonstrated that our method of practicing medicine needs reexamining. Conflicts of Interest None declared.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".