Patient Perceptions of Artificial Intelligence and Telemedicine in Dermatology: Narrative Review
Bibliographic record
Abstract
BACKGROUND: Artificial intelligence (AI) and telemedicine have great potential to transform dermatology care delivery, but patient perspectives on these technologies have not been systematically compared. OBJECTIVE: To examine patient perspectives on AI and telemedicine in dermatology to inform implementation strategies as these technologies increasingly converge in clinical practice. METHODS: A comprehensive literature search was conducted using PubMed, Scopus, and Embase databases between August 2024 and October 2024. We identified 48 articles addressing patient perspectives on AI and telemedicine in dermatology, with none directly comparing views on both technologies. RESULTS: Several distinct themes emerged regarding patient perspectives on these technologies: willingness to use, perceived benefits and risks, barriers to implementation, and conditions necessary for successful integration. Findings revealed that patients express hesitancy towards AI-based diagnoses that lack dermatologist involvement, while preferences for teledermatology varied by appointment reason, age, and prior technology exposure. Patients' motivations for AI implementation are connected to AI's potential for quicker diagnoses and improved triage efficiency, while telemedicine addresses logistical challenges such as reduced travel time and improved appointment availability. Both technologies were perceived to improve accessibility and diagnostic efficiency, though patients expressed concerns about AI's limited communication abilities and teledermatology's limits in performing physical examinations. Primary adoption barriers for these modalities included technological limitations and trust concerns, with patients emphasizing the need for dermatologist oversight, transparency, and adequate educational resources for successful integration. CONCLUSIONS: The complementary strengths of AI and teledermatology suggest they could mitigate each other's limitations when integrated-AI potentially enhancing teledermatology's diagnostic accuracy while teledermatology addresses AI's lack of human connection. By thoroughly examining these perspectives, this review may serve as a guide for patient-centered technological integration in the future landscape of accessible dermatologic care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".