{"title":"Artificial Intelligence in Skin and Wound Care: Enhancing Diagnosis and Treatment With Large Language Models.","authors":"Scott Nelson, Briana Lay, Alton R Johnson","doi":"10.1097/ASW.0000000000000353","DOIUrl":null,"url":null,"abstract":"<p><strong>Abstract: </strong>Artificial intelligence (AI) is revolutionizing the landscape of skin and wound care by improving diagnostic accuracy, treatment effectiveness, and patient outcomes. Artificial intelligence-driven tools, including machine learning models and large language models (LLMs), enhance the precision of wound assessments, facilitate early infection detection, and streamline clinical workflows. In addition, these tools may aid in patient symptom reporting, bridging the communication gap between patients and health care providers. Current AI applications include image recognition for wound classification, patient-facing symptom-checking chatbots, and personalized treatment recommendations. The integration of AI technologies not only supports better clinical decision-making but also empowers patients through improved access, engagement, and education. These tools are currently aimed at supporting clinical decision-making, not replacing clinicians. Moving forward, the expansion of AI capabilities in skin and wound care holds great promise, driving cost-effective, scalable, and equitable health care solutions.</p>","PeriodicalId":7489,"journal":{"name":"Advances in Skin & Wound Care","volume":"38 9","pages":"457-461"},"PeriodicalIF":1.4000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Skin & Wound Care","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/ASW.0000000000000353","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/9/2 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"DERMATOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract: Artificial intelligence (AI) is revolutionizing the landscape of skin and wound care by improving diagnostic accuracy, treatment effectiveness, and patient outcomes. Artificial intelligence-driven tools, including machine learning models and large language models (LLMs), enhance the precision of wound assessments, facilitate early infection detection, and streamline clinical workflows. In addition, these tools may aid in patient symptom reporting, bridging the communication gap between patients and health care providers. Current AI applications include image recognition for wound classification, patient-facing symptom-checking chatbots, and personalized treatment recommendations. The integration of AI technologies not only supports better clinical decision-making but also empowers patients through improved access, engagement, and education. These tools are currently aimed at supporting clinical decision-making, not replacing clinicians. Moving forward, the expansion of AI capabilities in skin and wound care holds great promise, driving cost-effective, scalable, and equitable health care solutions.
期刊介绍:
A peer-reviewed, multidisciplinary journal, Advances in Skin & Wound Care is highly regarded for its unique balance of cutting-edge original research and practical clinical management articles on wounds and other problems of skin integrity. Each issue features CME/CE for physicians and nurses, the first journal in the field to regularly offer continuing education for both disciplines.