Frailty detection in older adults using speech‐based machine learning models : Potential of an accessible tool to limit neurocognitive disorders

Speech-based machine learning models appear as the first accessible and scalable method for early frailty screening. This scalable approach can facilitate timely interventions to restore robustness, ultimately reducing the risk of neurocognitive disorders. Implementing speech-based frailty classifiers in clinical settings may enhance preventive strategies for aging populations, optimizing care pathways and reducing neurocognitive risks. Further studies with larger, more diverse cohorts and advanced methodologies are essential to validate and generalize these promising findings in real-world contexts.