Mikołajczyk Maksym, Patrzyk Sebastian, Nieniewski Mariusz, Woźniacka Anna
Student Research Circle at the Department of Dermatology and Venereology, Medical University of Lodz, Lodz, Poland.
Department of Dermatology and Venereology, Medical University of Lodz, Lodz, Poland.
Postepy Dermatol Alergol. 2021 Oct;38(5):761-766. doi: 10.5114/ada.2020.101258. Epub 2021 Nov 5.
Artificial intelligence (AI) could offer equal, or even more accurate, diagnoses of melanoma than most dermatologists. However, the value of popular smartphone applications for diagnosing unpigmented skin lesions remains unclear.
To compare the diagnostic accuracy of a popular, free-to-use web application for automatic dermatosis diagnosis against expert diagnosis of selected skin diseases.
Skin lesion images of patients with verified diagnosis were collected using a smartphone and were diagnosed by the application. The AI provided five diagnoses of varying probability. For each patient, accuracy of the diagnosis was evaluated by three criteria, i.e. whether the expert diagnosis was matched by the most probable automated diagnosis, one of the top three diagnoses or one of the top five diagnoses. Reliability was analysed using intraclass correlation coefficients.
The chance of a correct diagnosis increased when more outcomes were considered and more samples of a skin condition were included. However, the probability of a diagnosis repeating for the same patient was below 25%. Reliability, sensitivity and specificity were insufficient for clinical purposes.
Although AI diagnostics are encouraging, there is also a large margin for improvement, and AI is not yet an adequate replacement for medical professionals.
人工智能（AI）在诊断黑色素瘤方面可能比大多数皮肤科医生更准确，甚至能提供同等准确的诊断。然而，流行的智能手机应用程序在诊断无色素性皮肤病变方面的价值仍不明确。
比较一款流行的、免费使用的皮肤病自动诊断网络应用程序与专家对选定皮肤病的诊断准确性。
使用智能手机收集已确诊患者的皮肤病变图像，并由该应用程序进行诊断。人工智能提供了五个概率不同的诊断结果。对于每位患者，通过三个标准评估诊断的准确性，即专家诊断是否与最可能的自动诊断结果、前三个诊断结果之一或前五个诊断结果之一相匹配。使用组内相关系数分析可靠性。
当考虑更多结果并纳入更多某种皮肤状况的样本时，正确诊断的几率会增加。然而，同一患者诊断结果重复出现的概率低于25%。可靠性、敏感性和特异性对于临床目的而言不足。
尽管人工智能诊断令人鼓舞，但仍有很大的改进空间，并且人工智能尚未成为医学专业人员的充分替代品。
