Comparison of visual diagnostic accuracy of dermatologists practicing in Germany in patients with light skin and skin of color
Bibliographic record
Abstract
Abstract Visual clinical diagnosis of dermatoses in people of color (PoC) is a considerable challenge in daily clinical practice and a potential cause of misdiagnosis in this patient cohort. The study aimed to determine the difference in visual diagnostic skills of dermatologists practicing in Germany in patients with light skin (Ls) and patients with skin of color (SoC) to identify a potential need for further education. From April to June 2023, German dermatologists were invited to complete an online survey with 24 patient photographs depicting 12 skin diseases on both Ls and SoC. The study's primary outcomes were the number of correctly rated photographs and the participants’ self-assessed certainty about the suspected visual diagnosis in Ls compared to SoC. The final analysis included surveys from a total of 129 dermatologists (47.8% female, mean age: 39.5 years). Participants were significantly more likely to correctly identify skin diseases by visual diagnostics in patients with Ls than in patients with SoC (72.1% vs. 52.8%, p ≤ .001, OR = 2.28). Additionally, they expressed higher confidence in their diagnoses for Ls than for SoC (73.9 vs. 61.7, p ≤ .001). Therefore, further specialized training seems necessary to improve clinical care of dermatologic patients with SoC.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".