Artificial intelligence for neurodegenerative experimental models

INTRODUCTIONExperimental 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.METHODSHere we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.RESULTSConsidering 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.DISCUSSIONAI 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.HighlightsThere 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.

Automated summary

This article reviews the application of artificial intelligence (AI) and machine learning (ML) in dementia research. The challenges of reproducibility and cross-species translation are discussed, and the potential of AI and ML methods to enhance research and translation is evaluated. Data resources and AI approaches are highlighted as solutions, and the exciting future possibilities of multi-omics analysis with AI in drug discovery are mentioned.