Abstract
Ageing is the primary risk factor for many chronic, degenerative, and life-threatening disorders, yet the translational pipeline for geroprotective interventions remains comparatively sparse. Short‑lived, experimentally tractable models with conserved ageing pathways, particularly Caenorhabditis elegans, Drosophila melanogaster, and the African turquoise killifish (Nothobranchius furzeri), have expanded discovery beyond traditionally mammalian-centric pipelines. By leveraging advances in automation, high-content imaging, and artificial intelligence (AI), these models have shifted the field from low-throughput, reductionist assays to scalable, mechanistically informed in vivo phenotypic discovery. Here, we review recent advances in middle- to high-throughput screening (HTS) technologies across these models, review key phenotypic and molecular biomarkers, such as motility, cognition and memory, intestinal integrity, mitochondrial function, and immune response, and discuss their strengths and limitations. We further evaluate the expanding role of AI from in silico screening, automated and high-content phenotyping, to integrative multi-layer mechanistic inference. Key challenges, including data standardisation, reproducibility across laboratories, limited cross‑species pharmacokinetic comparability, AI model interpretability, and the translational gap between invertebrate hits and vertebrate or mammalian efficacy, are also discussed. By highlighting recent developments in in vivo disease models, HTS methodologies, and AI integration, this review provides a comprehensive resource for developing effective models and screening strategies to accelerate therapeutics for ageing and age-related diseases.
