“…Even if the best methods to capture the ground truth remain debatable, and the problems of induction explained by Lauc 128 are ignored, the intrinsic ability of ML to falsify prediction rules that lack empirical adequacy 133 strengthened by the increasing availability of big data 13,[164][165][166]229 could be leveraged to develop ML models that continuously integrate and assign specific weights (i.e., importance) to personal (e.g., clinical, radiological, histopathological, laboratory medicine, multi-omics, self-reported, and collected with wearable devices) and population-based empirical data (e.g., related to "social determinants of health") 97,[167][168][169][170][171]229 to predict health outcomes dynamically. 122,123,139,[172][173][174] In some of these models, hidden information extracted with ML models from WSIs that have shown to be valuable for prediction purposes 41,91,92,[94][95][96][97][98] will most likely obtain high weights.…”