Artificial intelligence for the study of human ageing: a systematic literature review
| datacite.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.contributor.author | Bernal, Mary Carlota | |
| dc.contributor.author | Batista, Edgar | |
| dc.contributor.author | Martínez-Ballesté, Antoni | |
| dc.contributor.author | Solanas, Agusti | |
| dc.date.accessioned | 2025-02-07T13:14:26Z | |
| dc.date.available | 2025-02-07T13:14:26Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | As society experiences accelerated ageing, understanding the complex biological processes of human ageing, which are affected by a large number of variables and factors, becomes increasingly crucial. Artificial intelligence (AI) presents a promising avenue for ageing research, offering the ability to detect patterns, make accurate predictions, and extract valuable insights from large volumes of complex, heterogeneous data. As ageing research increasingly leverages AI techniques, we present a timely systematic literature review to explore the current state-of-the-art in this field following a rigorous and transparent review methodology. As a result, a total of 77 articles have been identified, summarised, and categorised based on their characteristics. AI techniques, such as machine learning and deep learning, have been extensively used to analyse diverse datasets, comprising imaging, genetic, behavioural, and contextual data. Findings showcase the potential of AI in predicting age-related outcomes, developing ageing biomarkers, and determining factors associated with healthy ageing. However, challenges related to data quality, interpretability of AI models, and privacy and ethical considerations have also been identified. Despite the advancements, novel approaches suggest that there is still room for improvement to provide personalised AI-driven healthcare services and promote active ageing initiatives with the ultimate goal of enhancing the quality of life and well-being of older adults. | eng |
| dc.format.mimetype | ||
| dc.identifier.citation | Bernal, M., Batista, E., Martínez-Ballesté, A. et al. Artificial intelligence for the study of human ageing: a systematic literature review. Appl Intell 54, 11949–11977 (2024). https://doi.org/10.1007/s10489-024-05817-z | eng |
| dc.identifier.doi | https://doi.org/10.1007/s10489-024-05817-z | |
| dc.identifier.issn | 15737497 (Electrónico) | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/16231 | |
| dc.language.iso | eng | |
| dc.publisher | Springer Nature | spa |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | eng |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.source | Applied Intelligence | eng |
| dc.source | Vol. 54 (2024) | spa |
| dc.subject.keywords | Ageing | eng |
| dc.subject.keywords | Artificial intelligence | eng |
| dc.subject.keywords | Machine learning | eng |
| dc.subject.keywords | Ageing datasets | eng |
| dc.subject.keywords | Elderly | eng |
| dc.subject.keywords | Older adults | eng |
| dc.title | Artificial intelligence for the study of human ageing: a systematic literature review | eng |
| dc.type.driver | info:eu-repo/semantics/article | |
| dc.type.spa | Artículo científico | spa |
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| oaire.version | info:eu-repo/semantics/publishedVersion |