Photography and Image Acquisition in Dermatology a Scoping Review: Techniques for High-Quality Photography
Bibliographic record
Abstract
BACKGROUND: Clinical photography is essential in dermatology, in particular in the areas of surgical and cosmetic dermatology and has been evolving rapidly. However, many dermatologists desire more training in clinical photography and a comprehensive literature review of photography in dermatology is lacking. OBJECTIVE: This scoping review aimed to summarize the literature regarding techniques for high-quality photography in dermatology. MATERIALS AND METHODS: A literature search was conducted using Embase, MEDLINE, PubMed, and Evidence-Based Medicine databases in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. RESULTS: This review summarizes information from 74 studies. Camera type, resolution, lens choice, camera settings, environment and set-up, standardization, and types of clinical photography are all important factors in acquisition of high-quality photography. CONCLUSION: Photography in dermatology is continuously evolving with broader applications. Improved practices and innovations will benefit the quality of images.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".