Attitudes Toward Artificial Intelligence Among Dermatologists in Morocco: A National Survey
Bibliographic record
Abstract
Background Artificial intelligence (AI) is a hot topic, and the use of AI in our day-to-day lives has increased exponentially. AI is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding that of dermatologists in the diagnosis of skin lesions from clinical and dermoscopic images. However, little is known about the attitudes of dermatologists in Morocco toward AI. Objective The purpose of this cross-sectional study was to evaluate the attitudes of dermatologists in Morocco toward AI. Methods An online survey was distributed through Google Forms (Google LLC) to dermatologists in Morocco and was open from January to June 2021. Statistical analysis of the data collected was performed using Jamovi software. Any association for which the P value was <.05 was considered statistically significant. Results In total, 183 surveys were completed and analyzed. Overall, 79.8% of respondents were female, and the median age was 35 years (IQR 25-74 years). A total of 30.6% stated that they were not aware of AI, and 34.4% had a basic knowledge of AI technologies. Only 7.7% of the respondents strongly agreed that the human dermatologist will be replaced by AI in the foreseeable future. Of the entire group, 61.8% agreed or strongly agreed that AI will improve dermatology, and 70% thought that AI should be part of medical training. In addition, only 32.2% reported having read publications about AI. Female dermatologists showed more fear pertaining to the use of AI within dermatology (P=.01); this group also suggested that AI has a very strong potential in the detection of skin diseases using dermoscopic images (P=.03). Conclusions Our results demonstrate an overall optimistic attitude toward AI among dermatologists in Morocco. The majority of respondents believed that it will improve diagnostic capabilities. Conflict of Interest None declared.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".