Artificial intelligence for neurodegenerative experimental models

Abstract

Introduction: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.

Methods: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.

Results: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability.

Discussion: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.

Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

Keywords: FAIR; animal models; artificial intelligence; comparative biology; dementia; experimental models; iPSC; in silico; in vitro; in vivo; machine learning; neurodegeneration; preclinical; reproducibility; translation.