Machine Learning for Geriatric Clinical Care: Opportunities and Challenges

Abstract

To the Editor, Increasing life expectancy and geriatric-related changes pose major challenges for healthcare. 1) Geriatric patients experience many disorders including chronic diseases, weakness, cognitive decline, and functional dependence in the last two decades of life. As these problems can lead to hospitalization, these patients require quality clinical care. 2) Therefore, effective strategies for improving geriatric clinical care are essential. 1) One strategy to improve patient and clinical team outcomes, reduce costs, and enhance the health of patients is the use of artificial intelligence (AI). 3) AI can play key roles in the prevention, diagnosis, and treatment of patients' problems and the provision of healthcare. 4) AI has been used to detect cancer, disease management using robot-related technologies, provide screening tools to identify the risks of falls and urinary tract infections in geriatric patients with dementia, and assist healthcare providers in clinical decision-making and patient monitoring. 5) AI fills the gap of human resources in geriatric clinical care, reducing the burden on their family caregivers and, ultimately, improving the quality of care and life of older adults. 6) Machine learning (ML) is one of the main components of AI that uses statistical techniques to allow computer programs to make decisions and predictions based on previous data and experiences. 6) Recently, special attention has been paid to ML in the medical literature. Thus far, ML has been used to identify older people at high risk for dementia; predict weakness, risk of falls, pneumonia, delirium, and acute kidney injury; and provide geriatric clinical care to prevent these problems. 7) Moreover, these well-developed models show accuracy surpassing that of humans. 8) Many opportunities exist for the implementation of ML to improve geriatric care in the clinical setting. These opportunities include clinical task automation, optimizing clinical decision-making and support in practice, expanding clinical capacity, improving the safety level of geriatric patients, and increasing the quality of their

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