Store-and-Forward Images in Teledermatology: Narrative Literature Review
Bibliographic record
Abstract
Background: Store-and-forward (SAF) teledermatology uses electronically stored information, including patient photographs and demographic information, for clinical decision-making asynchronous to the patient encounter. The integration of SAF teledermatology into clinical practice has been increasing in recent years, especially during the COVID-19 pandemic. Despite this growth, data regarding the outcomes of SAF teledermatology are limited. A key distinction among current literature involves comparing the quality and utility of images obtained by patients and trained clinicians, as these metrics may vary by the clinical expertise of the photographer. Objective: This narrative literature review aimed to characterize the outcomes of SAF teledermatology through the lens of patient- versus clinician-initiated photography and highlight important future directions for and challenges of the field. Methods: . Only studies published after 2001 in English were included. In total, 47 studies were identified from the PubMed electronic database and Google Scholar after omitting duplicate articles. Results: Image quality and diagnostic concordance are generally lower and more variable with patient-submitted images, which may impact their decision-making utility. SAF teledermatology can improve the efficiency of and access to care when photographs are taken by either clinicians or patients. The clinical outcomes of clinician-submitted images are comparable to those of in-person visits in the few studies that have investigated these outcomes. Coinciding with the onset of the COVID-19 pandemic, asynchronous teledermatology helped minimize unnecessary in-person visits in the outpatient setting, as many uncomplicated conditions could be adequately managed remotely via images captured by patients and referring clinicians. For the inpatient setting, SAF teledermatology minimized unnecessary contact during dermatology consultations, although current studies are limited by the heterogeneity of their outcomes. Conclusions: In general, photographs taken by trained clinicians are higher quality and have better and more relevant diagnostic and clinical outcomes. SAF teledermatology helped clinicians avoid unnecessary physical contact with patients in the outpatient and inpatient settings during the COVID-19 pandemic. Asynchronous teledermatology will likely play a greater role in the future as SAF images become integrated into synchronous teledermatology workflows. However, the obstacles summarized in this review should be addressed before its widespread implementation into clinical practice.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".