From Teledermatology to Dermatology Artificial Intelligence: Will Teledermatology Exist in the Next 2 Years?
Bibliographic record
Abstract
Background Dermatology has been proven to be well suited for store-and-forward telemedicine triaging. With the reduced cost of computer power and readily available deep convolutional neural networks, using the digital images collected with store-and-forward, machine learning has made it possible to create artificial intelligence (AI) models. The AI models can analyze new digital images taken with a smartphone camera and return reliable dermatology outputs within seconds. Objective The aim of this study is to demonstrate the shift from teledermatology to dermatology AI. Methods A literature search was conducted and experience from a web-based teledermatology service was also considered. Results There has been a slow uptake of teledermatology in a clinical setting and by consumers. The development of AI dermatology models has gained momentum over the last few years. Studies have shown that AI dermatology is on par with teledermatology to deliver accurate diagnoses for the most common dermatology pathologies. Conclusions Teledermatology has still not gained mass adoption, both clinically and directly by consumers. The fast pace of AI dermatology development indicates that the technology will surpass store-and-forward teledermatology as a first point of digital consultation in dermatology. Conflicts of Interest AB is the owner of iDoc24 Inc.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".