Assessing public interest in artificial intelligence in dermatology: A Google Trends analysis
Bibliographic record
Abstract
Artificial intelligence (AI) is rapidly transforming dermatology, particularly through diagnostic imaging and enhancing patient management. Despite expanding clinical applications, public engagement with AI in dermatology remains underexplored. This study addresses this gap by analyzing public interest in AI dermatology over the past decade using Google Trends. Search terms were categorized into groups of "AI dermatology," "general AI," "AI nondermatology," "general dermatology," and "general nondermatology." Monthly Search Volume Index values from January 2015 to January 2025 were collected, and linear and exponential regression models quantified temporal trends. Geographic analysis evaluated the frequency of countries appearing in the top five search volumes for each term. Public interest in AI dermatology terms increased markedly after 2022, with growth of 73.6%, 143.6%, and 59.1% in 2022, 2023, and 2024, respectively. AI dermatology terms demonstrated a steeper linear slope (6.212) compared with general AI (6.181) and dermatology terms (1.61), and an exponential growth factor of 0.551. Interest was highest in Singapore, Ireland, Australia, the Philippines, New Zealand, and the United Arab Emirates. These findings indicate a substantial rise in global engagement with AI in dermatology and highlight the importance of integrating public interest considerations into AI tool development, clinical practice, patient safety, and equitable access.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.023 | 0.036 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".