Traditional test characteristics, such as sensitivity, specificity, and their derivatives, are important considerations when evaluating a new medical test. However, these characteristics do not necessarily indicate the clinical value of the test, which includes balancing the potential harms and benefits of testing.1 This is particularly salient for sentinel lymph node (SLN) biopsy, a staging procedure for melanoma in which most patients receive negative test results and may experience associated harms of the procedure. In this issue of JAMA Dermatology, Marchetti et al2 use decision curve analysis methods to determine the net benefit of using i31 gene expression profiling (GEP), a commercially available neural network–based model that combines molecular information from GEP with clinicopathological information to predict SLN biopsy positivity. In the case of this study, after bringing benefits and harms on the same standardized scale (so that they were comparable), the decision curve analysis considered the benefit of identifying SLN positivity against the potential harms of SLN biopsies and, from this, estimated a net benefit. While i31-GEP may have improved test characteristics compared with GEP alone, when factoring in net benefit, the authors found that the test was favorable for use in patients with T1b (more benefit than harm), but unfavorable in patients with T1a (more harm than benefit). These types of decision analysis studies are important for patients and clinicians to understand the trade-offs associated with new tests. Nevertheless, further research in the form of prospective trials is still necessary to understand the benefits and harms associated with using i31-GEP or any other novel test.