Photography in dermatology ‐ a scoping review: Practices, skin of color, patient preferences, and medical‐legal considerations
Bibliographic record
Abstract
Clinical photography is essential in dermatology. However, a comprehensive literature review of photography in dermatology is lacking. This scoping review aims to summarize the literature regarding photography practices in dermatology, photography of skin of color, patient preferences, and medical-legal considerations. A search was conducted utilizing Embase, MEDLINE, PubMed, and Evidence Based Medicine databases in accordance with the PRISMA extension for Scoping Reviews. In total, 33 studies were summarized. Clinical photography is commonly used in biopsy site marking, assessment, diagnosis, disease monitoring, evaluation of treatment response, medical education, research, seeking advice from colleagues, and teledermatology. Although dermatologic photography remains devoid of skin of color representation, photographic considerations for darker skin are available. Most patients support medical photography, with a preference for clinical photographs to be taken by their own physicians, and for use of clinic/hospital-owned cameras over personal devices. Pertinent medical-legal issues include concerns around privacy, personal device use, and documentation of consent. Photography in dermatology is continuously evolving with broader applications. Improved practices and innovations will benefit individuals of various skin tones. Management of consent and privacy must be upheld to sustain the increasing ease of image capture and sharing.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| 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) | 0.005 | 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".