Affiliations: [a] Department of Internal Medicine and Medical Specialties (DIMI), Section of Geriatrics, University of Genoa, Genoa, Italy
| [b] IRCCS Ospedale Policlinico San Martino, Genoa, Italy
Correspondence:
[*]
Correspondence to: Fiammetta Monacelli, Associate Professor in Geriatrics, Department of Internal Medicine and Medical Specialties (DIMI), University of Genoa, Viale Benedetto XV, 6, 16132, Genoa, Italy. Tel.:/Fax: +39 01053351055; E-mail: fiammetta.monacelli@unige.it.
Abstract: A recent study by Ding et al. explores the integration of artificial intelligence (AI) in predicting dementia risk over a 10-year period using a multimodal approach. While revealing the potential of machine learning models in identifying high-risk individuals through neuropsychological testing, MRI imaging, and clinical risk factors, the imperative of dynamic frailty assessment emerges for accurate late-life dementia prediction. The commentary highlights challenges associated with AI models, including dimensionality and data standardization, emphasizing the critical need for a dynamic, comprehensive approach to reflect the evolving nature of dementia and improve predictive accuracy.
Keywords: Alzheimer’s disease, dementia risk, machine learning, precision medicine
