(PI-003) A Literature Review on Explainable Artificial Intelligence (XAI) and Chronic Wound Management
Thursday, May 16, 2024
7:30 PM – 8:30 PM East Coast USA Time
Thanoon Thabet, MSN, RN, WTA-C – New York - Presbyterian Westchester
Introduction: Chronic wounds represent a global health challenge, affecting approximately 7 million Americans annually and an estimated 1 to 2% of the population worldwide. These include pressure injuries, impacting around 2.5 million individuals globally, diabetic foot ulcers, which develop in 15 to 20% of patients with diabetes, and venous leg ulcers, which account for 60% of chronic wound complications. Such prevalence underscores an urgent need for predictive models that can refine clinician identification, management, and treatment outcomes. Explainable Artificial Intelligence (XAI) emerges as an innovative approach in healthcare, proposing machine learning algorithms that elucidate their reasoning, thus enhancing clinician trust. This literature review aims to integrate findings on XAI's role in chronic wound management, focusing on its contribution to improving diagnostics, treatment, and prognostic accuracy in healing.
Methods: A literature search was conducted from October to December 2023 using PubMed, Cochrane, Google Scholar, MEDLINE, and databases. Keywords included "chronic wounds," "explainable artificial intelligence (XAI)," "pressure injuries," "diabetic foot ulcers," and "venous leg ulcers." The retrieval yielded a total of 60 articles. Inclusion criteria encompassed peer-reviewed articles in English, published between 2020 and 2023, and related to XAI in chronic wound management. Articles that did not address chronic wound care or were outside the scope of XAI were excluded, leaving 6 articles for review.
Results: A thorough literature review revealed that Explainable Artificial Intelligence (XAI) significantly enhances chronic wound care by improving diagnostic accuracy, personalizing treatment plans, and providing reliable prognostic assessments. These themes collectively underscore XAI's potential to transform clinical practices by enabling a deeper understanding of AI-driven predictions and decisions in chronic wound management.
Discussion: Applying Explainable Artificial Intelligence (XAI) principles in chronic wound management ignites a potential leap forward in the quality of patient care. Through XAI, the rationale behind machine learning predictions and decisions can be made transparent, increasing the accuracy of early detection and enabling the creation of personalized treatment plans. Future research should be dedicated to the clinical validation of AI models developed under the XAI framework, ensuring they meet ethical standards for patient care and creating user-friendly interfaces.