Applications of machine learning in solving memory loss issues
Ruibin L
Memory loss disorders such as Alzheimer’s disease lack effective treatments and leave less flexibility for time allotted for interventions. Machine learning (ML) offers a solution by detecting early biomarkers, enabling personalized therapies, and even restoring neural function through brain-computer interfaces (BCIs). However, ethical and technical challenges remain, such as the resistance mentality of the elderly. This essay evaluates ML’s potential to transform neurodegenerative disease management while addressing its limitations. Memory loss arises from multiple causes, including neurodegenerative diseases like Alzheimer's, natural ageing processes, and traumatic brain injuries. 1 Current clinical approaches mainly rely on pharmaceutical interventions such as cholinesterase inhibitors and non-pharmacological therapies like cognitive rehabilitation. 2 However, these methods show little progress in slowing disease progression and manage symptoms without treating the root cause of the disease. Traditional diagnostic tools also identify memory disorders at advanced stages only when significant neural damage has occurred. 3 These limitations highlight the need for more precise solutions. ML models present various possible approaches, particularly those that analyse neuroimaging data, which demonstrate potential for earlier and more accurate detection of memory-related conditions. 4 Advanced algorithms can identify patterns in brain scans that may predict memory problems years before symptoms appear. 5 Furthermore, ML provides the potential for personalized treatment by analysing individual patient data and predicting how patients will likely respond to treatments (note 5). This represents progress beyond current standard treatments that do not account for individual differences in memory disorders (note 4).
1 See Alzheimer's Association. ’2023 Alzheimer's Disease Facts and Figures.’ Alzheimer's & Dementia 19, no.
4 (2023). Accessed 7th July 2025. https://www.alz.org/alzheimers-dementia/facts-figures. 2 See Rajpurkar, P. al. ’AI in Radiology: Key Studies and Clinical Applications.’ Radiology 294, no. 3 (2020). Accessed 30th June 2025. https://pubs.rsna.org/doi/10.1148/radiol.2020191853. 3 See Hampstead, B. et al. ‘Machine Learning Identifies Neuroimaging... Early-Stage Alzheimer's.’ Journal of Neuroscience 40, no. 42 (2020). Accessed 7th July 2025. https://www.jneurosci.org/content/40/42/7946. 4 See Kording, K. and B. Hampstead. ‘Machine Learning in Clinical Neuroscience: Foundations and Applications’. Springer, 2021. https://link.springer.com/book/10.1007/978-3-030-63543-8. 5 Esteva, A. et al. ‘Deep Learning for Medical Image Analysis: A Guide for Clinicians.’ Nature
Medicine 25(1):24–29. Accessed July 7, 2025. https://www.nature.com/articles/s41591-018-0316-z.
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