ReviewIndian dermatology online journal
Artificial Intelligence in Diagnostic Dermatology: Challenges and the Way Forward.
Review in Indian dermatology online journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Revolutionizing dermatopathology using AI in skin diagnostics: scoping review.Frontiers in medicine · 2026Pooled it
- Cosmetogenomics unveiled: a systematic review of AI, genomics, and the future of personalized skincare.Frontiers in artificial intelligence · 2025Pooled it
- Multimodal Large Language Models in Dermatologic Diagnosis: A Game Changer or a Clinical Risk?Acta dermato-venereologica · 2026Article
- Agentic Artificial Intelligence in Dermatology.International journal of dermatology · 2026Article
- Emerging non-invasive tools for hair follicle assessment: a narrative review on hyperspectral and terahertz imaging.Lasers in medical science · 2026Review
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Assessing public interest in artificial intelligence in dermatology: A Google Trends analysis.JID innovations : skin science from molecules to population health · 2026Article
- Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives.Bioengineering (Basel, Switzerland) · 2025Review
- Policy Challenges in Ultra-Rare Cancers: Ethical, Social, and Legal Implications of Melanoma Prevention and Diagnosis in Children, Adolescents, and Young Adults.Healthcare (Basel, Switzerland) · 2025Article
- Real-world evidence and the future of personalized medicine: a global perspective on data, ethics, and equity.Frontiers in health services · 2025Review
- Next-generation approach to skin disorder prediction employing hybrid deep transfer learning.Frontiers in big data · 2025Article
- Assessing the Impact of ChatGPT in Dermatology: A Comprehensive Rapid Review.Journal of clinical medicine · 2024Review
- From diagnosis to intervention: a review of telemedicine's role in skin cancer care.Archives of dermatological research · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) has emerged as a transformative force in the field of diagnostic dermatology, offering unprecedented capabilities in image recognition and data analysis. Despite its promise, the integration of AI into clinical practice faces multifaceted challenges that span technical, ethical, and regulatory domains. This article provides a narrative overview of the current state of AI in dermatology, tracing its historical evolution from early diagnostic tools to contemporary hybrid supervised models. We identify and categorize six critical challenges: data quality and quantity, algorithmic development and explainability, ethical considerations, clinical workflow integration, regulatory frameworks, and stakeholder collaboration. Each challenge is dissected from the perspectives of academia, industry, and healthcare providers, offering actionable recommendations for future research and implementation. We also highlight the paradigm shift in AI research, emphasizing the potential of transformer architectures in revolutionizing diagnostic methodologies. By addressing the challenges and harnessing the latest advancements, AI has the potential to significantly impact diagnostic accuracy and patient outcomes in dermatology.
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.