Data Sheet 1_Blood biomarkers and artificial intelligence: toward precision medicine in Alzheimer’s disease.pdf

In this paper, we synthesize recent advancements in the application of machine learning (ML) on blood biomarker analysis, evaluating their diagnostic efficacy and clinical translational potential. Current evidence identifies plasma p-tau217 as the best individual biomarker for primary screening.

However, multiple biomarker signatures (such as A/T/N) combined with ML algorithms (like XGBoost) greatly improve diagnostic accuracy for mild cognitive impairment. Notably, predictive performance is maximized with multimodal fusion, which integrates data from blood, neuroimaging, and genetics, achieving an area under the curve of up to 0.94. To mitigate the black-box nature of these models, explainable AI frameworks like SHapley Additive exPlanations (SHAP) are essential for establishing clinical interpretability.