Chen Adrian, Qilleri Aleksandra, Foster Timothy, Rao Amit S, Gopalakrishnan Sandeep, Niezgoda Jeffrey, Oropallo Alisha
At the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, United States, Adrian Chen, BS, Aleksandra Qilleri, BS, and Timothy Foster, BS, are Medical Students. Amit S. Rao, MD, is Project Manager, Department of Surgery, Wound Care Division, Northwell Wound Healing Center and Hyperbarics, Northwell Health, Hempstead. Sandeep Gopalakrishnan, PhD, MAPWCA, is Associate Professor and Director, Wound Healing and Tissue Repair Analytics Laboratory, School of Nursing, College of Health Professions, University of Wisconsin-Milwaukee. Jeffrey Niezgoda, MD, MAPWCA, is Founder and President Emeritus, AZH Wound Care and Hyperbaric Oxygen Therapy Center, Milwaukee, and President and Chief Medical Officer, WebCME, Greendale, Wisconsin. Alisha Oropallo, MD, is Professor of Surgery, Donald and Barbara Zucker School of Medicine and The Feinstein Institutes for Medical Research, Manhasset New York; Director, Comprehensive Wound Healing Center, Northwell Health; and Program Director, Wound and Burn Fellowship program, Northwell Health.
Adv Skin Wound Care. 2024;37(11&12):601-607. doi: 10.1097/ASW.0000000000000226.
Generative artificial intelligence (AI) models are a new technological development with vast research use cases among medical subspecialties. These powerful large language models offer a wide range of possibilities in wound care, from personalized patient support to optimized treatment plans and improved scientific writing. They can also assist in efficiently navigating the literature and selecting and summarizing articles, enabling researchers to focus on impactful studies relevant to wound care management and enhancing response quality through prompt-learning iterations. For nonnative English-speaking medical practitioners and authors, generative AI may aid in grammar and vocabulary selection. Although reports have suggested limitations of the conversational agent on medical translation pertaining to the precise interpretation of medical context, when used with verified resources, this language model can breach language barriers and promote practice-changing advancements in global wound care. Further, AI-powered chatbots can enable continuous monitoring of wound healing progress and real-time insights into treatment responses through frequent, readily available remote patient follow-ups.However, implementing AI in wound care research requires careful consideration of potential limitations, especially in accurately translating complex medical terms and workflows. Ethical considerations are vital to ensure reliable and credible wound care research when using AI technologies. Although ChatGPT shows promise for transforming wound care management, the authors warn against overreliance on the technology. Considering the potential limitations and risks, proper validation and oversight are essential to unlock its true potential while ensuring patient safety and the effectiveness of wound care treatments.
生成式人工智能(AI)模型是一项新的技术发展成果,在医学各专科领域有着广泛的研究应用案例。这些强大的大语言模型在伤口护理方面提供了广泛的可能性,从个性化的患者支持到优化治疗方案以及改进科学写作。它们还可以帮助高效地浏览文献、选择和总结文章,使研究人员能够专注于与伤口护理管理相关的有影响力的研究,并通过快速学习迭代提高回复质量。对于非英语母语的医学从业者和作者来说,生成式人工智能可能有助于语法和词汇选择。尽管有报告指出对话代理在医学翻译方面存在局限性,涉及对医学语境的精确解读,但与经过验证的资源一起使用时,这种语言模型可以突破语言障碍,推动全球伤口护理领域改变实践的进步。此外,人工智能驱动的聊天机器人可以通过频繁、便捷的远程患者随访,实现对伤口愈合进展的持续监测和对治疗反应的实时洞察。然而,在伤口护理研究中实施人工智能需要仔细考虑潜在的局限性,尤其是在准确翻译复杂的医学术语和工作流程方面。在使用人工智能技术时,伦理考量对于确保可靠和可信的伤口护理研究至关重要。尽管ChatGPT在改变伤口护理管理方面显示出前景,但作者警告不要过度依赖这项技术。考虑到潜在的局限性和风险,适当的验证和监督对于释放其真正潜力、确保患者安全和伤口护理治疗的有效性至关重要。