ReviewJMIR dermatology2025
Patient Perceptions of Artificial Intelligence and Telemedicine in Dermatology: Narrative Review.
Review in JMIR dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Dermatology Among Saudi Adults: Cross-Sectional Survey Study.Healthcare (Basel, Switzerland) · 2026Article
- The double-edged sword of generative AI in dermatology: a multi-component cross-sectional study on physician burnout, patient satisfaction, and communication quality.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) and telemedicine have significant potential to transform dermatology care delivery, but patient perspectives on these technologies have not been systematically compared. Objective: This study aimed 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 papers addressing patient perspectives on AI and telemedicine in dermatology, with none directly comparing patients' views of 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 toward AI-based diagnoses that lack dermatologist involvement, while preferences for teledermatology varied by reason for appointment, age, and previous technology exposure. Patients' motivations for implementing AI are connected to its potential for quicker diagnoses and improved triage efficiency. At the same time, 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 inability to perform 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 the patient-centered integration of technology in the future landscape of accessible dermatologic care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.