Table 1.

Overview of current applications, challenges and prospects for machine learning and AI applications in five key areas of dementia research

	Current areas of machine learning and AI applications	Challenges and knowledge gaps	Prospects and future directions
Genetics	Full genomic signal analysis [3, 4] Statistical fine mapping [6, 7] Single cell genomics [9, 10] Identification of causal variants [6]	Effect of specific genetic variants [5] Relation of genetic variation to cellular changes [5] Mixed evidence for interaction of genetics with modifiable risk [21−24]	Utilisation of integrative data sets [124] Combining omics data to identify functional implications [125] Application of genetic risk to individuals [15]
Experimental Medicine	Data-driven multimodal analysis [35, 36] Gene regulation [26, 27] Digital twin brain models link [33]structure, function and pathology	Translational gap from models to human disease biology [126] Lack of power in small, single modality studies [126] Poor reproducibility [127]	Efficient drug target discovery [128] Simulated ageing signatures [129] Digital brains for precision dementia research34
Drug discovery and Trials Optimisation	Intelligent drug target identification [16] Incorporation of multiple biomarker data [59] Natural language processing and text mining of electronic health records [68]	Heterogeneity of disease risk, severity and subtype [60, 61] Cost of longitudinal analysis [60] Restricted access to clinical trial data [64]	Enhanced identification of risk for trial recruitment [63] Utilising publicly available data and linked health records [16, 66] Multi institutional collaborative initiatives to share data [67]
Neuroimaging	Automated feature extraction for diagnosis and prediction [85] Combining imaging modalities and biomarker data [86] Investigation of disease progression and biological mechanisms [61, 87]	Lack of clinical implementation [85] Poor interpretability is challenging for regulation [91] Sensitivity to bias in the training data [91]	Validation of existing models for clinical settings [90] Availability of large data sets and repositories [85] Strategic recruitment to improve real-world applicability [91]
Prevention	Analysis of complex interactions in observational studies [113] Increased accuracy of polygenic risk and predictive models [15, 130] Validation of drug repurposing for dementia prevention [117, 118]	Inconsistent evidence for many potential risk factors [93, 95–97] Causal relationships poorly understood [98] Lack of statistical power [17]	Personalised dementia prevention interventions [122, 123] Deep learning for improved Mendelian randomisation [103, 105] Lifespan modelling to identify the optimal timing of a prevention intervention