Smith Payton, Johnson Chandler E, Haran Kathryn, Orcales Faye, Kranyak Allison, Bhutani Tina, Riera-Monroig Josep, Liao Wilson
Department of Dermatology, University of California San Francisco, San Francisco, CA, USA.
Dermatology Department, Hospital Clínic de Barcelona, Universitat de Barcelona, Barcelona, Spain.
Curr Dermatol Rep. 2024 Sep;13(3):141-147. doi: 10.1007/s13671-024-00434-y. Epub 2024 Jun 13.
Machine learning (ML), a subset of artificial intelligence (AI), has been vital in advancing tasks such as image classification and speech recognition. Its integration into clinical medicine, particularly dermatology, offers a significant leap in healthcare delivery.
This review examines the impact of ML on psoriasis-a condition heavily reliant on visual assessments for diagnosis and treatment. The review highlights five areas where ML is reshaping psoriasis care: diagnosis of psoriasis through clinical and dermoscopic images, skin severity quantification, psoriasis biomarker identification, precision medicine enhancement, and AI-driven education strategies. These advancements promise to improve patient outcomes, especially in regions lacking specialist care. However, the success of AI in dermatology hinges on dermatologists' oversight to ensure that ML's potential is fully realized in patient care, preserving the essential human element in medicine.
This collaboration between AI and human expertise could define the future of dermatological treatments, making personalized care more accessible and precise.
机器学习(ML)作为人工智能(AI)的一个子集,在推进图像分类和语音识别等任务方面发挥了至关重要的作用。它融入临床医学,尤其是皮肤科,在医疗服务方面实现了重大飞跃。
本综述探讨了机器学习对银屑病的影响——银屑病的诊断和治疗严重依赖视觉评估。该综述强调了机器学习正在重塑银屑病治疗的五个领域:通过临床和皮肤镜图像诊断银屑病、皮肤严重程度量化、银屑病生物标志物识别、精准医学强化以及人工智能驱动的教育策略。这些进展有望改善患者的治疗效果,尤其是在缺乏专科护理的地区。然而,人工智能在皮肤科的成功取决于皮肤科医生的监督,以确保机器学习的潜力在患者护理中得到充分发挥,同时保留医学中至关重要的人文因素。
人工智能与人类专业知识之间的这种合作可能会定义皮肤病治疗的未来,使个性化护理更容易获得且更精确。