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

Area of Science:

• Biomedicine
• Artificial Intelligence
• Neurodegenerative Diseases

Background:

• Alzheimer's disease research is complex, with fragmented findings and competing hypotheses.
• Limited translational success hinders progress in understanding and treating the disease.

Purpose of the Study:

• To propose AI as a tool for epistemic reconciliation in Alzheimer's disease research.
• To align disparate data, methods, and mechanistic insights into coherent models.
• To shift the paradigm in understanding Alzheimer's disease emergence, progression, and treatment.

Main Methods:

• Utilizing AI for reconciliation, not just predictive performance.
• Developing flexible, testable digital twin architectures grounded in homeostasis and multiscale coherence.
• Employing an iterative, interoperable AI architecture to integrate evidence and resolve contradictions.

Main Results:

• AI facilitates the alignment of diverse data and mechanistic insights.
• Digital twins serve as testable architectures for understanding disease dynamics.
• The approach highlights critical knowledge gaps in Alzheimer's research.

Conclusions:

• AI-driven reconciliation offers a paradigm shift for Alzheimer's disease research.
• This framework moves beyond incremental progress to transform scientific understanding and applications.
• Reconciliation is presented as a guiding principle for advancing Alzheimer's science.