Background: Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM. Methods: A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases. Results: A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%. Conclusion: Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians’ work is supported by AI, facilitating management decisions and improving health outcomes.
背景：皮肤黑色素瘤在全球范围内持续增加公共卫生负担，尤其在浅肤色人群中尤为显著。先进技术，特别是人工智能（AI），可能为临床医生提供一种辅助工具，以更高的准确率帮助检测恶性肿瘤。本系统综述旨在报告市售卷积神经网络（CNNs）在检测黑色素瘤方面的性能指标。方法：通过CINAHL、Medline、Scopus、ScienceDirect和Web of Science数据库进行系统性文献检索。结果：共纳入16篇报告黑色素瘤的文章。检测到的黑色素瘤总数为1160例，非黑色素瘤病变为33,010例。市场批准的技术与临床医生在黑色素瘤分类方面的性能存在高度异质性，灵敏度范围为16.4%至100.0%，特异性介于40.0%至98.3%之间，准确率在44.0%至92.0%之间。当临床医生与AI协同工作时，观察到较低的异质性，灵敏度介于83.3%至100.0%，特异性在83.7%至87.3%之间，准确率在86.4%至86.9%之间。结论：与其关注AI与临床医生在黑色素瘤分类方面的性能对比，临床医生在AI支持下工作时获得了更一致的性能，这有助于管理决策并改善健康结果。
肿瘤(癌症)患者之家