Mining the gaps: Deciphering Alzheimer's biology through AI-driven reconciliation

Alzheimer's disease remains one of the most complex and contested domains in biomedicine, characterized by fragmented findings, competing hypotheses, and limited translational success. We propose that AI can offer not just technical acceleration but a deeper epistemic contribution: reconciliation. Rather than optimizing predictive performance or replicating existing assumptions, the goal is to align disparate data, methods, and mechanistic insights into coherent models that explain how the disease emerges, progresses, and can be treated. This approach centers on digital twins, not as monolithic models, but as flexible, testable architectures grounded in homeostasis, destabilization, and multiscale coherence. Through an iterative, interoperable AI architecture, digital twins integrate evidence, resolve contradictions, and highlight where critical gaps remain. This framework moves beyond incremental progress within the prevailing model to catalyzing a paradigm shift in how Alzheimer's is understood. Reconciliation, in this sense, is not a method but a guiding principle for transforming both the science and its applications.