Liopyris Konstantinos, Gregoriou Stamatios, Dias Julia, Stratigos Alexandros J
1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece.
Dermatology Department, Memorial Sloan Kettering Cancer Center, New York, NY, 10021, USA.
Dermatol Ther (Heidelb). 2022 Dec;12(12):2637-2651. doi: 10.1007/s13555-022-00833-8. Epub 2022 Oct 28.
Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians' diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices.
基于机器学习和卷积神经网络（CNN）的人工智能在皮肤病学领域正迅速成为一个现实的前景。非黑色素瘤皮肤癌是全球最常见的癌症，而黑色素瘤是最致命的癌症形式之一。皮肤镜检查提高了医生识别皮肤癌的诊断准确性，但遗憾的是，其准确性仍然相对较低。人工智能可以在皮肤癌的早期评估和诊断中提供宝贵的帮助。在过去十年中，人工智能领域的新研究和出版物取得了突破。研究表明，与临床医生相比，CNN算法能够从皮肤镜图像中对皮肤病变进行分类，其性能更优或至少相当。尽管人工智能算法在读者研究中对皮肤癌诊断显示出非常有前景的结果，但其在日常临床实践中的可推广性和适用性仍然难以捉摸。在此，我们试图总结读者研究中强调的人工智能的潜在缺陷和挑战，并确定未来研究中克服局限性的策略。最后，我们试图分析人工智能在我们的实践中的潜在应用为皮肤病学和患者带来更美好未来的优势和机遇。
