AI-based PD-L1 scoring shows concordance with expert assessment in 333 NSCLC cases.

This cohort study included 333 non-small cell lung cancer (NSCLC) cases to assess the concordance of PD-L1 expression scores. The primary comparison was between an AI-based scoring method, specifically the uPath VENTANA PD-L1 (SP263) Assay Algorithm, and assessment by an expert pathologist. The study setting and specific follow-up duration were not reported in the available data.

The main results regarding the degree of concordance between the AI algorithm and expert assessment were not reported in the provided input. Consequently, specific statistical measures such as kappa coefficients, percentage agreement, or sensitivity analyses could not be included in this summary. Safety data, including adverse events, discontinuations, or tolerability, were also not reported.

Key limitations of this evidence include the absence of reported quantitative outcomes and the lack of information regarding the specific clinical setting or patient demographics beyond the case count. Because the primary finding is not reported, the extent to which the AI tool aligns with expert consensus remains unknown based on this text. Practice relevance is constrained by these data gaps, preventing a definitive assessment of the tool's utility for routine clinical use or its potential to reduce inter-observer variability in this specific cohort.