AI-Assisted Dermatology in Provider Shortage Areas: A Systematic Review of Access and Wait Time Outcomes.

introductionAccess to dermatologic care remains a persistent challenge in the United States, particularly in rural and underserved areas. Delays in dermatologic evaluation and treatment are compounded by provider shortages, long wait times, and geographic barriers. Emerging tools such as artificial intelligence (AI), AI-assisted triage, and teledermatology platforms might offer scalable solutions to improve access and reduce delays. This article evaluates whether AI-assisted technology, compared to traditional in-person dermatology care, shortens wait times to less than 30 days for patients living in provider shortage areas.

methodsA systematic review was conducted between March and June 2025 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and academic library databases were queried using the following Boolean queries: "Artificial intelligence triage dermatology" and "dermatology AND access AND teledermatology AND care AND wait times." Studies were screened for relevance, and 41 met the inclusion criteria. A narrative synthesis was used due to heterogeneity in study designs and outcome measures. Each study was appraised using the Joanna Briggs Institute (JBI) critical appraisal tools.

resultsIncluded studies demonstrated that AI-assisted technologies, particularly when integrated into teledermatology systems, significantly reduced dermatology wait times, often to fewer than 30 days. Store-and-forward platforms enabled expedited triage, while AI-supported decision tools improved diagnostic accuracy (85-97% sensitivity) and reduced unnecessary referrals. Task shifting to nonspecialist providers with AI support was found to be safe and effective. Despite promising outcomes, concerns related to image quality, algorithmic bias, and uneven implementation remain.

conclusionAI-assisted dermatologic tools show strong potential to improve access to care and reduce wait times in provider shortage areas. These technologies could support timely diagnosis, streamline referrals, and enable safe task shifting to primary care teams. Importantly, findings highlight the role of nurse practitioners (NPs), particularly those with limited dermatology training, in leveraging AI as both an educational and clinical decision support tool. By providing differential diagnoses, confidence scores, and visual explanations, AI can strengthen NP diagnostic confidence, reduce unnecessary referrals, and expand access to timely dermatologic care in underserved settings. Future research should focus on implementation in resource-limited settings, nurse-led AI triage models, and long-term health outcomes.