The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks

Abstract

Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems in this field. The topic is very important because the telemedicine and AI applied to dermatology have the opportunity to improve both the quality of healthcare for citizens and the workflow of healthcare professionals. This study conducted an overview on the opportunities, the perspectives, and the problems related to the integration of TD with AI. The methodology of this review, following a standardized checklist, was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using parameters with five levels of score. The outcome highlighted that applications of this integration have been identified in various skin pathologies and in quality control, both in eHealth and mHealth. Many of these applications are based on Apps used by citizens in mHealth for self-care with new opportunities but also open questions. A generalized enthusiasm has been registered regarding the opportunities and general perspectives on improving the quality of care, optimizing the healthcare processes, minimizing costs, reducing the stress in the healthcare facilities, and in making citizens, now at the center, more satisfied. However, critical issues have emerged related to: (a) the need to improve the process of diffusion of the Apps in the hands of citizens, with better design, validation, standardization, and cybersecurity; (b) the need for better attention paid to medico-legal and ethical issues; and (c) the need for the stabilization of international and national regulations. Targeted agreement initiatives, such as position statements, guidelines, and/or consensus initiatives, are needed to ensure a better result for all, along with the design of both specific plans and shared workflows.

The Past, the Present and the Future of Teledermatology: A Narrative Review

“… 67 It also raises ethical and medico-legal issues that need to be addressed at an international level. 68 …”

Public Perceptions, Factors, and Incentives Influencing Patient Willingness to Share Clinical Images for Artificial Intelligence-Based Healthcare Tools

“…Another major advantage of AI lies in its ability to continuously learn and stay up-to-date with the latest medical knowledge, leading to ongoing improvement in supporting dermatologists. Future potential for AI in dermatology includes personalized treatment plans tailored to each patient’s history and presentation, discovering clinical patterns in diseases, assessing risk of disease development and proactively suggesting appropriate screening measures, accelerating drug development, and empowering patients by providing them with real-time insights into their health [ 21 , 22 ].…”

“…While our study provided insights into factors influencing public hesitations towards image sharing for AI, future studies are necessary to explore potential solutions that encourage patient participation in contributing to the development of more representative AI datasets. Other weaknesses of image-based AI include limited integration of patient history into image analysis, limited data sharing between institutions, variability in image quality, and a lack of a standardized protocol for recruiting patient images for AI [ 12 , 21 ].…”

“…Previous studies have investigated patient perspectives regarding image sharing for another increasingly popular medical technology: teledermatology [ 23 , 24 ]. While some teledermatology may incorporate AI to enhance decision-making support, not all teledermatology services use AI [ 21 ]. It is important to recognize that teledermatology and AI are two distinct entities, and patients who use teledermatology are sharing images for their own diagnosis and evaluation.…”

Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis

“…AI has made significant recent advancements in teledermatology (TD), a progress that the COVID-19 pandemic has notably accelerated. This merging of AI and TD is heralding a transformation in remote dermatological care, potentially enhancing diagnostic accuracy and promoting equitable healthcare access ( 47 ). AI’s image analysis and data interpretation capacity empowers healthcare professionals to employ these tools for remote dermatological diagnosis and treatment, ensuring patient care quality and safety.…”