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Applications of Artificial Intelligence to aid detection of dementia: a narrative review on current capabilities and future directions

With populations ageing, the number of people with dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases are due to Alzheimer's disease (AD) pathology and there is a 10-20 year 'pre-clinical' period before significant cognitive decline occurs. We urgently need, cost effective, objective methods to detect AD, and other dementias, at an early stage. Risk factor modification could prevent 40% of cases and drug trials would have greater chances of success if participants are recruited at an earlier stage. Currently, detection of dementia is largely by pen and paper cognitive tests but these are time consuming and insensitive to pre-clinical phases. Specialist brain scans and body fluid biomarkers can detect the earliest stages of dementia but are too invasive or expensive for widespread use. With the advancement of technology, Artificial Intelligence (AI) shows promising results in assisting with detection of early-stage dementia. Existing AI-aided methods and potential future research directions are reviewed and discussed.

commonly used "quick" screening tests. Table 1 shows the comparative performance of the cognitive screening tests described.

Mini-Mental State Examination (MMSE)
The MMSE [17], first published in 1975, takes 6 to 10 minutes to complete and produces a score out of 30. It measures different aspects of cognition including memory, orientation, language, attention, and visuospatial function. Folstein et al. [17], screened two cohorts for dementia-related diseases, using the MMSE and found significant agreement between MMSE score and dementia diagnosis by clinicians. The mean score for patients with dementia was 9.7, for those with depression and cognitive impairment 19.0, and for controls 27.6. O'Bryant et al. [18] and Crum et al. [19] presented the distribution of MMSE scores by educational and age level and the MMSE has been translated and validated in different regions including Spain [20,21], China [22,23], France [24,25], Korea [26], Brazil [27], and Greece [28].
The specificity of the MMSE for detecting AD has been calculated as approximately 0.80 -0.90 but the main drawback is its low sensitivity, especially for detecting early-stage dementia [29]. It has proved difficult to set a threshold cutoff score to achieve both high sensitivity and specificity simultaneously. For example, the sensitivity/specificity of the MMSE has been estimated at 0.98/0.32 when a cut-off score of 29 is used, and 0.50/1.00 when a cut-off score is set to 22 [30].

Addenbrooke's Cognitive Examination-Revised (ACE-R)
The ACE-R is a more extensive cognitive screening test designed by Mioshi et al. [31] and modified from an earlier version of Addenbrooke's Cognitive Examination (ACE) test [32] to include additional visuospatial components. It takes 15-20 minutes to complete, is scored out of 100, and assesses attention, memory, fluency, language and visuospatial function. Nasreddine et al. [31], applied ACE-R to 142 participants with dementia (67 with AD, 55 with FTD and 20 with LBD), 36 participants with MCI and a control group of 63 participants. The ACE-R achieved a sensitivity/specificity of 0.94/0.89 with a cut-off score of 88, and 0.84/1.00 when the cut-off was set at 82. In addition, ACE-R has been adapted and translated for use in several different countries including Germany [33,34], China [35,36], Japan [37,38], Korea [39], Brazil [40], Spain [41], Portugal [42], and France [43].

Montreal Cognitive Assessment (MoCA)
The MoCA is scored out of 30 points and takes approximately 10 minutes to complete. It tests short-term memory, visuospatial function, executive function, phonemics fluency, attention and language. When Nasreddine et al. [16] evaluated MoCA in 93 participants with AD, 94 participants with MCI and 90 healthy controls, it had sensitivity/specificity reaching 1.0/0.87 with a cut-off score of 26. Similar to other screening tests, the MoCA has been adapted into various language versions [44,45,46,47,48,49].
The main drawbacks for all three of these cognitive tests are the need for a clinician, or other trained person, to administer the test and interpret the results, and the time taken to complete these processes. Furthermore, with any test that relies solely on total test score, there is a substantial clinical data about cognitive processes that is simply discarded -for example the recall of a list of objects may be correct (and score full marks) but the delayed time to recall these items, and pauses and hesitations in doing so, is crucial (but unrecorded) information that suggests deficits compared to someone who quickly recalls the same list.

Clock drawing test
In this deceptively simple and quick test, the person is asked to draw the contour of a clock, write in all the numbers and then draw the clock hands at a particular defined time point (e.g., "ten to five"). The total score for the drawing is based on the accuracy of the contour and the positioning of the numbers and hands and this test assesses executive, spatial and visuo-constructive cognitive functions [50].
Shulman et al. [51] reviewed clock drawing tests and concluded that mean sensitivity/specificity was approximately 0.85/0.85. Watson et al's. [52] adapted clock drawing test requires participants to draw clock numbers in a pre-drawn circle and the number of digits in each quadrant is counted to calculate the error, achieving a sensitivity/specificity of 0.87/0.82 for dementia. Although clock drawing tests are relatively quick to administer and achieve similar sensitivity and specificity to other longer cognitive screening tests, the quality of the clock drawn by participants has not been appropriately considered in most cases. For example, there are likely to be subtle differences during the process of drawing, such as slowing down, pausing, hesitations, etc. that signal early changes of dementia, but all this data is disregarded in the simple total score analysis.  [52] In summary, the limitations or the current methods for screening for cognitive deficits associated with dementia are the time taken, the need for a trained person to administer and interpret the test, the reduction of performance to a total score that loses crucial information on cognitive decline, and the lack of sensitivity to the earliest stages of dementia. These same issues also apply to detailed 'gold standard' neuropsychological tests to a greater or lesser degree, although the trade-off for greater sensitivity/specificity is also greater time consumption and need for an expert to administer. There thus remains a need for new automated, objective, accurate and cost-effective methods that analyse not only the outcome at the end of testing but also cognitive performance during the process.

Artificial Intelligence (AI) Based Tests
Recently, various AI-based tests have been introduced to assist with the detection of dementia -either through detecting functional changes (such as cognitive, movement or speech impairments) or through detecting pathological abnormalities on brain scans. AI-based tests can provide improved accuracy through their capability of capturing additional features from a large amount of data. This brings out more objective inference compared with clinicians' manually analyzed results [53]. Furthermore, AI provides an automated analysis process -both in terms of time and cost efficiencies [54]. Recently introduced AI-based tests include computerized cognitive tests, computer-assisted interpretation of brain scans, and movement -and speech-analysis tests. None of these have yet reached routine clinical practice and largely remain in the research setting. Here we discuss the techniques used and the relative accuracies of the various approaches.

Computerized Cognitive Tests
In recent years, computerized cognitive tests have been introduced for screening and detection of dementia [55,56]. They collect data on participants' behaviour during the tests as well as their total scores. Other benefits include the potential to be user-directed, negating the need for a researcher or clinician to administer it -thus reducing costs, improving accessibility, and avoiding inter-rater variability.
CogState is a computerized, web-based battery that measures visuomotor function, psychomotor speed, attention, memory, execution function and social cognition [57]. It is easy to understand for testers having less computer using experience. It also provides customized batteries which are subsets of the general CogState battery to satisfy customized requirements. Several works examine the validity of CogState battery. Maruff et al. [58]  With the help of machine learning technologies, additional features can be extracted, which improves the speed and accuracy of the screening process, but the main drawback is the requirement for a tablet -this may affect its usability and accessibility. Table 2 shows the performance of different computerized cognitive screening tests.

Movement Tests
Another emerging approach for screening dementia is to analyse patterns of voluntary movement as motor control is known to deteriorate in dementia [67,68,69,70,71,72]. Gait [73,74], hand movements [75], and eye movements are the three main types of movement that have been investigated.
Riona et al. [76] tried to differentiate AD, DLB and PDD through gait analysis by using a accelerometer-based wearable. Participants were required to perform six 10m walks at their comfortable pace with the wearable setting on the skin above the fifth lumbar vertabra. Fourteen gait features representing pace, variability, rhythm, asymmetry and postural control have been extracted to discriminate three cases. According to the AUC metric, only variability and asymmetry features can significantly discriminate AD from PDD. Chung et al. [77] set similar walking task for 12 participants (9 AD and 3 healthy controls) to do the gait analysis. Participants were required to walk along straight line of 40m without speed limit and a triaxial accelerometer was set on the foot to extract gait features. The result showed different spatiotemporal gait parameters between AD and healthy controls but without statistical analysis. Few studies have used machine learning methods to analyse gait. Beauchet et al. [73] found that gait analysis could differentiate between motoric cognitive risk (MCR), a pre-dementia syndrome characterized by slow gait and cognitive complaints [78], healthy people and people with dementia. In their experiment, participants were divided into MCR class and non-MCR class. Gait speed combined with other data including participant's cognitive test results, multiple logistic regression model and statistical tests were used to determine the relationship between MCR syndrome and brain volumes. The results showed that for executive function, there was significant difference between MCR and non-MCR groups.
Motor control of the hands and dexterity is also known to change in dementia and AI methods have been applied in a research setting. For example, Sano et al. [79] used a magnetic sensor-based device to measure participants' fingertapping waveforms and extracted 15 features for one-class SVM. The model achieved a recall of over 95% to classify normal subjects from those with dementia. Liang et al. [80] investigated two hand trajectory tracking methods to assist in screening deaf individuals for signs of dementia. The first method was based on skin colour segmentation, and the second method was based on part affinity fields OpenPose Skeleton Model. According to the comparison experiments, the second method showed more enhanced and robust performance on tracking hand trajectory in terms of the distance between prediction trajectory and ground truth trajectory in a coordinate system.
Computer-assisted graphic drawings and handwriting tests are an alternative method to examine hand motor function. Yu et al. [81] designed several graphic drawing and handwriting tasks to assess the differences in motor function between AD, MCI and healthy controls. Movement fluency features (such as pause time per stroke and the ratio of in air to on paper time) and handwriting accuracy related features, such as character size variation and stroke orientation control, were extracted. These features were combined with personal information compared between the groups. The result showed that AD and MCI groups had significantly larger variation and errors than healthy subjects in terms of the control of line drawing. Impedovo et al. [66] designed a protocol which integrated caregiver interview, a traditional cognitive test (MMSE) and a set of computerized handwriting tasks. Handwriting features including time stamp, x-y coordinates, and mean pressure were also extracted and fed into the random forest classifier to achieve an average precision of 0.72, recall of 0.73 and F-measure of 0.71.
Eye movement tests have also been explored as a non-invasive technique for the early detection of dementia [82,83,84,85,86]. For example, in [87] healthy people, people with subjective cognitive impairment (SCI) and people with MCI read aloud and then read silently whilst their eye movements were measured by EyeLink 1000 Desktop Mount with monocular eye-tracking and a headrest for head stabilization. Thirteen eye movement features including saccade amplitude and total fixations were extracted, and then machine learning-based classifiers such as Naive Bayes model, SVM and logistic regression (LR) model were applied. The Naive Bayes model achieved the best accuracy of 86.0% to discriminate MCI, SCI and normal. In another study, and eye-tracking system was proposed to monitor 15 participants' (9 older controls, 3 young controls and 3 MCI) cognitive function through a memory test and an eye movement task. Eye movement task required participants to watch four short videos with eyes fixating on one object during the video. The same 13 features were extracted as in [87], and fed into least square regression, ridge regression and LASSO regression to perform the classification. The LASSO regression model achieved the highest accuracy to predict memory ability based on eye movement with a mean absolute error of 5.52 [88].
Unlike most cognitive tests, movement-based tests offer the opportunity to examine for declining brain functions using a method free of language. This is an attractive feature when considering accessibility of tests to populations around the world. However, this field of research is still somewhat in its infancy and constrained by the need generally for additional equipment to measure the body movements accurately. It may be that 'smart' environments solve this issue in the future (See Section IV below).

Speech, Conversation, and Language Tests
Speech and language abilities are known to be impaired early in several types of dementia, with symptoms such as aphasia, pauses, reduced vocabulary and other language impairments [89,90,7]. Several studies have used AI-based speech and language tests for dementia screening. Common test processes include extracting related features and then feeding the features into machine learning or deep learning classifiers to detect the patterns consistent with dementia. There are mainly two types of features that can be extracted and analyzed, i.e., acoustic features and linguistic features. Acoustic features describe how people articulate speech -the actual sounds rather than the content. Common acoustic features include Mel-frequency cepstral coefficients (MFCC), speech rate, and cepstral peak prominence (CPP). Linguistic features describe the content, such as the vocabulary, grammar and syntax. Classical machine learning methods, inclusive of SVM, logistic regression model, random forest and KNN, have been applied to classify subjects into different categories based on the extracted features. Two main types of speech tests have been used: a picture description test and conversation, generated through an interview test.
For picture description tests [91,92,93,94], participants are asked to describe a picture in a fixed time and speech is recorded. Liu et al. [93] used this method and analyzed the spontaneous speech for screening dementia and MCI. Temporal features (e.g., duration of each utterance, number of syllables), acoustic features (e.g., energy, jitter and shimmer, MFCCs) and linguistic features (e.g., word counts) were extracted and then fed into a logistic regression classifier, achieving an accuracy of 81.9%. Likewise, Haider et al. [91] evaluated the performance of a model trained by purely acoustic features for detecting dementia. Data were collected from four speech tasks, inclusive of a description task, a word fluency task, a story recall task and a sentence construction task. Extracted acoustic features included eGeMAPS, emobase, ComParE and MRCG. Based on acoustic features, they used active data representation (ADR) to capture all speech segments information. KNN, linear discriminant analysis (LDA), random forests and SVM were used in classification experiments for dementia detection. The highest classification accuracy, of 77.4%, was achieved by using LDA on eGeMAPS features.
In conclusion, it has been shown that the data features of speech have a greater influence on accuracy than the machine learning method. Combining both linguistic features and acoustic features performs better than each on its own. Pan et al. [94] proposed an automatic hierarchical bidirectional attention neural network to detect AD based on a picture description task and their model achieved an accuracy of 84.0%. Chien et al. [92] used a convolutional recurrent neural network (CRNN) to generate feature sequences. The network achieved a sensitivity/specificity of 0.76/0.76.
In conclusion, deep learning techniques did not obviously show many advantages over machine learning in terms of classification accuracy. One possible reason can be the lack of data for deep learning method training.
The second approach to speech analysis for dementia detection is to analyse conversational speech generated through interviews [ [101] with the help of SHoUT diarization toolkit and automatic speech recognition (ASR) for extracting 12 acoustic features, 12 lexical features and 20 conversation analysis (CA)-based features based on interviews between patients and neurologists. The classification accuracy on discriminating between controls and FMD reached 90.0%. Weiner et al. [102] proposed a framework to detect early signs of dementia based on conversations, consisting of voice activity detection, speaker diarization, feature extraction and dementia detection. Speech pause-based features, speaking rate features, word error rate features and linguistic features from spoken dialogues through a computer avatar were extracted and fed to SVM and logistic regression models to distinguish Aging-Associated Cognitive Decline (AACD), AD and healthy controls. Tanaka et al. [95], Ujiro et al. [98] and Luz et al. [97] were similar works but used subjects' spoken dialogues with doctors to perform the analysis and found 86.5% -95.0% classification accuracy for controls vs dementia. Bullard et al. [103], Shao et al. [104] and Balbim et al. [105] attempted to combine speech, conversation and language data with other sources of data to improve detection performance. For example, Bullard et al. [103] fused linguistic data, such as bag-of-words (BOW), term frequency inverse document frequency (TF-IDF) and topics from latent Dirichlet allocation (LDA), with non-linguistic clinical data such as blood tests and cerebrospinal fluid biomarkers. These multi-modal features were fed into a logistic regression to classify subjects into AD, Late MCI, Early MCI and healthy control groups. The highest (MCI, AD and normal) classification accuracy reached 80.9%. Table 3 shows the comparative performance of computerized speech analysis tests.

Computer -Assisted Interpretation of Brain Scans
Currently, magnetic resonance imaging (MRI) and computed tomography (CT) scans are used to support the differential diagnosis that is formed though cognitive tests and clinical assessment. Brain scans provide information on structural changes of various brain regions that are known to be associated with different types of dementia. For example, clinicians will specifically look for atrophy (shrinkage) in a part of the brain called the hippocampus in AD, or in the frontal lobes in FTD. Positron emission tomography (PET) is a specialist and expensive brain scan, only accessible in certain centres, that provides information on function (blood flow and metabolism of brain regions) and can also measure underlying abnormal proteins such as amyloid (in AD). The presence of these changes provides additional evidence to support the clinical assessment, but the interpretation of the scans is somewhat subjective and vulnerable to inter-rater variability. Furthermore, specialist doctors are required to interpret the visual data of a scan -a precious resource that many people around the world do not have access to. Early-stage dementia may have only minimal, or no atrophy and so is often the most challenging scan to interpret as it sits on the cusp of normality. A computerized method to aid automated interpretation and measurement of brain scans would be highly advantageous to the process of diagnosing dementia.
Two main AI methods have been used to analyse brain scans: machine learning and deep learning. A recent systemic review [106] examined machine learning methods to classify subjects based on neuroimaging data.
Various machine learning models including AdaBoost, Gaussian process, logistic regression, random forest [107], support vector machine (SVM), and k nearest neighbour (kNN), have been applied to two large longitudinal data sets that together comprise interval MRI and PET scans from almost 2,000 participants with dementia, MCI or healthy controls: Alzheimer's Disease Neuroimaging Initiative (ADNI) [108] and Open Access Series of Imaging Studies (OASIS) [109]. For example, Mathotaarachchi et al. [107] focused on methods to predict the progression of MCI to dementia using ADNI image data. They pre-processed PET data using CIVET pipeline and the outputs of the pipeline were Florbetapir PET Standard Update Value Ratios (SUVR). Based on SUVR values, features were extracted by applying voxel-wise logistic regression analysis and a random forest-based classifier was used for training and prediction. The classifier achieved a sensitivity/specificity of 0.71/0.87 to determine MCI from dementia. Although machine learning based brain scan analysis has shown promising results in some studies, Pellegrini et al. [106] stated that generally it has proved hard to discriminate the early imaging changes of dementia with machine learning methods.
Recently, deep learning techniques have provided more promising results for detecting the subtle imaging changes associated with the earliest stages of dementia [110,111,112]. Yiugit et al. [113] used a CNN model to classify MRI scans from people with AD, MCI and healthy controls. Slices from the 3D MRI scans were extracted, data augmentation was performed, then three different neural network models with distinct numbers of convolutional layers were compared. The proposed method achieved a sensitivity/specificity of 0.93/0.81 to discriminate MCI from normal and 0.86/0.75 to discriminate AD from normal. There are also encouraging results that deep learning can differentiate different typs of dementia; for example Iizuka et al. [111] used a deep CNN model trained by brain PET scan data to distinguish DLB, AD and healthy controls; their model had a binary classification accuracy of 93.1% between DLB and controls, 89.3% between DLB and AD and 92.4% between AD and controls. Backstorm et al. [114] applied 3D CNNs on 3D MRI brain scans to classify healthy controls from individuals with AD. The model achieved an accuracy of 98.74%, with an AD detection rate of 100% and a false positive rate of 2.4%.
In summary, deep learning methods show promising results for accurately discriminating brain scans indicative of AD compared to healthy ageing. They also show promise to discriminate specific types of dementia. Rather than depending solely on clinicians' subjective judgement, deep learning approaches have strong potential to provide automated, objective measures and reduce the inter-and intra-subject variability. However, current challenges are that deep learning models require a huge amount of data for training and validating purposes. Furthermore, CNN-based models lack interpretability, to understand which precise features of the scan they base their models on. This "black box" approach may hinder acceptance by clinicians and integration into standard practice. Table 4 shows the computer-assisted method performance in analyzing medical scan images for detecting changes associated with dementia. 4 Future directions: combining AI methods and smart environments Smart environments and combinations of AI-based methods are exciting potential future research directions. Establishing smart environments is a novel approach that, in the future, could help detect changes of early dementia in people's own homes [116]. The so-called "smart" environment comprises different "intelligent", or AI-driven, sensors surrounding the living environment to monitor daily activities without imposing restrictions compared to using wearable sensors. There are two main categories of smart environment monitoring, i.e. real-life monitoring and scenario-based monitoring. Real-life monitoring refers to the situation that participants simply go about their usual daily activities with different sensors being installed around their home, while scenario-based monitoring refers to participants being asked to enter a research laboratory to simulate the real-life situation.
Batista [117] reviewed the detection of wandering patterns (a sign of early dementia) through a smart environment. Trajectory analysis was based on the position data, which were captured by GPS, Infra-Red and RFID proximity sensors. The movement data was fed into machine learning methods to classify wandering, from non-wandering patterns. They concluded that, due to the uncertainty of the environment, and uniqueness of individual behaviour patterns, detecting wandering behaviours in a smart environment is still a challenge. Khodabandehloo et al. [118] set ambient sensors to detect wandering behaviour by monitoring people's real-life activities. They adopted a collaborative learning technology to complete two tasks, i.e. trajectory segmentation and wandering episode detection. A series of position records captured by ambient sensors were used for trajectory segmentation, and wandering episodes were detected based on the trajectory segmentation by using machine learning methods. Among a range of machine learning methods, the random forest model achieved the highest overall accuracy of 80.7% for the case of personalized selection and 70.5% for the case of non-personalized selection.
In addition to wandering pattern detection, movement analysis in smart environments has also assisted detecting early signs of cognitive related disorders. Paolini et al. [119] set a smart environment for early detection of AD based on localization and movement analysis. RFID reader technology was used for indoor localization analysis, and a wearable inertial sensor system including accelerometer and gyroscope was used for movement analysis. Early detection of AD was further realized by evaluating indoor movement activities. Stavropoulos et al. [120] introduced a framework by integrating different ambient sensors' data to analyze movement and activities, which supported the care for people with dementia. Sensors included a camera, microphone, DTI-2 (a wearable device), and plugs, which collected image data, audio data, and motion data. They set different sensors in a room where a person with dementia lives. By using different types of sensors, different types of data could be extracted to monitor the person. Such heterogeneous data collected from different sensors were being processed at an analysis layer, then went to a representation layer to store the knowledge. After that, it went to an interpretation layer for semantic interpretation and fusion. Furthermore, it went to a service layer and an application layer for clinicians to use. For visual activity recognition, they used GMM clustering method to cluster specific activities. Fisher vectors were used for extracting fixed size features. Multiclass SVM model was used for the classification of different activities. On a dataset of 98 participants, the model achieved an average recall and precision of 82% for recognition of four activities (answering a phone, establishing account balance, preparing a drink, preparing drug box).
Similarly, Lotfi et al. [116] set passive infra-red sensors (PIR) around the home, such as door entry-point sensors, electricity power usage sensors, bed/sofa pressure sensors and flood sensors, to predict subjects' abnormal behaviour through movement analysis. Echo state network (ESN) was used to classify different behaviours. The root mean square error (RMSE) of training was around 1% and 7% for back door and kitchen sensors respectively.
In conclusion, using smart home environments to capture daily life behaviours may provide a completely new method in the future to detect the earliest changes associated with dementia. However, the complex environment and the diversity of events lead to more challenges. Privacy and acceptability to the public are other issues that need to be explored.

Conclusions
There is a growing field of research that applies AI technologies to aid the early detection of dementia screening tests. AI-based tests offer exciting opportunities to streamline the diagnostic process of dementia detection. They may assist clinicians by providing objective evidence of changes associated with the early stages of dementia and offer several advantages over traditional dementia screening tests. First, AI enables more features to be retrieved from a single test, which may improve the accuracy of dementia detection. For example, compared with traditional score-based cognitive tests, computerized cognitive tests can not only extract the final test results but also the richer process-related features. Second, AI helps reduce errors due to subjective judgements. For example, how a patient performs in a conversation test is largely dependent on the interviewee's subjective opinion at that time but AI makes the inference process more objective. Third, AI shifts the automation of dementia screening to a higher level and this is particularly attractive to epidemiology studies and to public health bodies who want to target risk reduction interventions at an early stage. AI is quick to analyze and respond to large population screening compared with doctors' judgement alone.
We propose two main future directions for AI-based dementia screening: smart environments and non-invasive multiple tests integration. The smart environment can monitor candidates' behaviour in various aspects of daily life by different environmental sensors. Current smart environment applications generally assess participants whilst they perform specific tasks or tests. In the future, the smart environment could play a greater role in monitoring people's behaviour in normal daily life and recognise personalized change over time offering potential for a precision approach to detection and monitoring of dementia.
Another key future direction will be overcoming the challenge of how to integrate different dementia screening tests -to make the multi-modal tool as accurate as possible. AI-based dementia tests so far have been designed to work separately. For example, language tests are designed to screen dementia in terms of language abilities, while cognitive tests focus on functions like memory and attention. It would be particularly synergistic to see speech and motion analysis combined in smart environments, especially as we know that dual motor-speech tasks tend to be impaired early in dementia [121]. Furthermore, a strength of AI, especially deep learning, is integrating results from different tests to find the most discriminating patterns.
The challenges in the design and the implementation processes AI-based dementia screening tests are critical. For example, how do we design a comprehensive, easily accessible, and swift protocol that integrates different types of data to provide accurate detection of dementia? Perhaps the hardest challenge of all will be opening up the black box models of AI so that providers, clinicians and users alike feel they can trust the outcomes and understand the findings.