{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:20:34Z","timestamp":1790958034655,"version":"4.1.0"},"reference-count":224,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/X005674\/1"],"award-info":[{"award-number":["MR\/X005674\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017510","name":"UK Dementia Research Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100017510","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000957","name":"Alzheimer's Association","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000269","name":"Economic and Social Research Council","doi-asserted-by":"publisher","award":["ES\/W010240\/1"],"award-info":[{"award-number":["ES\/W010240\/1"]}],"id":[{"id":"10.13039\/501100000269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000864","name":"Michael J. 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However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p>Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>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\u2010model reproducibility and translation to human biology, while sustaining biological interpretability.<\/jats:p><\/jats:sec><jats:sec><jats:title>DISCUSSION<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Highlights<\/jats:title><jats:p><jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>There are increasing applications of AI in experimental medicine.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>We identified issues in reproducibility, cross\u2010species translation, and data curation in the field.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Our review highlights data resources and AI approaches as solutions.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Multi\u2010omics analysis with AI offers exciting future possibilities in drug 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