Artificial Intelligence (AI) in Geriatric Diagnostics

Abstract

The use of Artificial Intelligence (AI) in geriatric diagnostics is expanding rapidly, yet its integration remains uneven and often poorly adapted to the complexities of ageing populations. This chapter examines how AI systems are currently applied in diagnosing age-related conditions such as dementia, frailty, and multi-morbidity. It assesses the strengths and limitations of machine learning models trained predominantly on younger or non-representative data, raising concerns about diagnostic bias, false positives, and underdiagnosis in older adults. The chapter also explores the ethical, practical, and regulatory challenges of implementing AI in clinical environments where human judgment, patient history, and social context remain critical. By focusing on transparency, data quality, and inclusive model design, it argues for a cautious but deliberate rethinking of how AI can serve, not replace, clinical expertise in geriatric care.

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