Comprehensive Summary
Liu et al. performed a meta-analysis of various studies discussing the use of deep learning to diagnose basal cell carcinoma (BCC). Databases like PubMed, Embase, and Web of Science were searched to find applicable articles, and overall performance and chance of bias were analyzed for each source. Out of the 1,941 sources chosen, results illustrated that the deep learning models used to assist in diagnosis demonstrated a higher area under the curve than dermatologists alone. Overall, the meta-analysis performed by Liu et al. provided insight into the improved efficiency of deep learning-driven diagnoses compared to conventional diagnosis by medical doctors.
Outcomes and Implications
Though some caution should be taken before generalizing the results of the study, as each article exhibits slightly different standards, the research performed in this meta-analysis highlights how deep learning models overall improve the diagnostic performance and accuracy compared to dermatologists alone. In future applications, deep learning models can make diagnosis of BCCs more efficient and less time-consuming while still providing high accuracy.