Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Australia.
Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
J Invest Dermatol. 2024 Jun;144(6):1200-1207. doi: 10.1016/j.jid.2023.11.007. Epub 2024 Jan 16.
Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.
人工智能(AI)算法在皮肤病变分类方面的表现已经达到甚至超过了专家皮肤科医生在实验环境下的准确度。然而,大多数算法并不能代表实际的临床方法,因为在实际临床中会考虑皮肤表型和临床背景信息。我们回顾了目前皮肤病变分类的 AI 技术,并提出了将其应用于全身摄影(TBP)分析时的机遇和挑战。在 TBP 分析中应用 AI 可以通过整合患者级别的元数据来实现对患者皮肤表型的个体化评估和整体风险评估,但在算法开发中保护患者隐私和改进可解释 AI 方法方面仍存在挑战。