SynthesisFrontiers in digital health2025
Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.
Synthesis in Frontiers in digital health, 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
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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.
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Who cites it
2 citing papers in PubMed.
- Article
- Comparative Analysis of Prescriptions and Pharmacy Services in Internet-Based Psychiatric Hospital During and After the COVID-19 Pandemic: Retrospective Cross-Sectional Observational Study.Journal of medical Internet research · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The diagnosis of skin diseases can be challenging due to their diverse manifestations, while early detection of malignant skin cancers greatly improves the prognosis, highlighting the pressing need for efficient screening methods. In recent years, advancements in AI have paved the way for AI-aided diagnosis of skin lesions. Furthermore, the COVID-19 pandemic has spurred the demand of telemedicine, accelerating the integration of AI into medical domains, particularly in China. This article aims to provide an overview of the progress of AI-aided diagnosis in Chinese dermatology. Given the widespread use of public datasets in the reviewed studies, we compared the performance of AI models in segmentation and classification on public datasets. Despite the promising results of AI in experimental settings, we recognize the limitations of these public datasets in representing clinical scenarios in China. To address this gap, we reviewed the studies that used clinical datasets and conducted comparative analyses between AI and dermatologists. Although AI demonstrated comparable results to human experts, AI still cannot replace dermatologists due to limitations in generalizability and interpretability. We attempt to provide insights into improving the performance of AI through advancements in dataset quality, image pre-processing techniques, and integration of medical data. Finally, the role that AI will play in the medical practice and the relationship between AI and dermatologists are discussed. This systematic review addresses the gap in evaluating AI applications in Chinese dermatology, with a focus on dermatological datasets and real-world application.
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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.