Table 1 An overview of reviewed studies in the field of geriatrics and machine learning

Choudhury et al. [8]

To understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases

2020

Individuals over the age of 65 and have one or more chronic illnesses

35 studies

psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7), and the review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications

Olender et al. [10]

To examine the role machine learning in predicting clinical outcomes of older adults

2023

Older adults above age 65 years

37 studies

The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality

Leghissa et al. [11]

To identify studies based on frailty identification, detection and classification

2023

Frail older adults

41 studies

The data types can be divided into gait data, usually collected with sensors, and medical records, often in the context of aging studies. The most common algorithms are well-known models available from every Machine Learning library

Chowdhury et al. [9]

To identify the current application of machine learning and artificial intelligence in mental health disorders

2021

Studies focusing on electronic health records and administrative health data

21 articles

Electronic health records was the most used data type, and random forest was the most used ML algorithm

Baecker et al. [12]

1. to introduce the reader to the field of brain age prediction and highlight its clinical potential

2. to explain the most common methodological approaches to brain age prediction and discuss five promising clinical applications and possible next steps

2021

brain age prediction

–

Five promising clinical applications of ML-

1. Marker of general brain health, 2. Early detection of brain-based disorders, 3. Prognosis of brain-based disorders, 4. Differential diagnosis of brain-based disorders, 5. Treatment outcome

Questions and next steps-

1. Account for inter-scanner heterogeneity, 2. Increase granularity of brain age, 3. Dynamic changes of brain age

Fabris et al. [13]

To review the works that have used supervised ML to study the ageing process

2017

supervised machine learning applied to ageing research

–

The link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport