Beyond fluorescence: A critical look at AI-powered brightfield analysis for T-cell killing assays

This comment evaluates dela Cruz-Chuh et al.'s AI-based label-free workflow for analyzing T-cell mediated tumor killing via brightfield imaging. The study's core strengths include eliminating fluorescent labeling artifacts, achieving comparable consistency to conventional segmentation-based methods, and accommodating phenotypically diverse cancer cells without manual parameter tuning-addressing key bottlenecks in immunotherapy screening. However, critical considerations persist: the binary "killing/non-killing" classification framework may insufficiently resolve low-level or partial cytotoxicity, as evidenced by six false negatives; generalizability to non-adherent hematological malignancies or alternative effector cells remains untested; and the model lacks interpretability regarding morphological features driving cytotoxicity predictions. Additionally, performance across variable imaging platforms or culture conditions is unevaluated, limiting translational reproducibility. Despite these gaps, the workflow advances high-throughput immunotherapy screening efficiency. Future studies incorporating multi-class training, validation across diverse cancer types, and mechanistic decoding of AI-predicted features will strengthen its rigor and broader applicability in drug discovery.