A predictive mathematical framework for hemoglobin–material interactions: development of a hemoglobin sensitivity index (HSI) for next-generation biomedical applications

) of 0.943 and a root mean square error of 0.087 when validated against computational predictions and literature-derived cytotoxicity databases containing 1247 material-application pairs. Machine learning-enhanced parameter weighting revealed reactive oxygen species generation and binding affinity as dominant contributors with predicted weights of 0.247 ± 0.032 and 0.182 ± 0.023, respectively. Polymer-based material classes, including PEG polymers, exhibited predicted HSI values of 0.45-0.52, while carbon materials showed predicted risk profiles of 3.12-7.23. Application-specific optimization reduced the average predicted HSI by 67% compared to conventional designs. This HSI framework establishes the first quantitative, mechanistically grounded computational platform for predicting hemoglobin-material interactions, enabling rational biocompatible material design and supporting regulatory harmonization for accelerated clinical translation.