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INTRODUCTION We investigated the validity, feasibility, and effectiveness of a voice recognition‐based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large‐scale community of elderly participants. METHODS Eligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain‐specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale. RESULTS Among the 11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini‐Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA). DISCUSSION This study suggests that the DCS is an effective and efficient tool for dementia and MCI case‐finding in large‐scale cognitive screening. Highlights To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community‐dwelling elderly. With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores. The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences. The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation.

INTRODUCTION This study evaluated the reliability and validity of the Rhode Island Mobile Cognitive Assessment Tool (RIMCAT), a proctored digital cognitive screening test. METHODS One hundred older adult participants (healthy controls [HC] n = 50; cognitively impaired [CI], n = 50) completed the RIMCAT. RESULTS Test–retest reliability across 4 weeks was excellent. Internal consistency was high. RIMCAT total scores were significantly correlated with the Mini‐Mental State Examination and the Mattis Dementia Rating Scale. RIMCAT component scores were significantly correlated with corresponding in‐office neuropsychological measures. Logistic regression analysis of RIMCAT total scores correctly classified 77.9% of individuals as cognitively impaired or cognitively healthy. Receiver operating characteristic analysis revealed an optimal sensitivity of 84.8% and specificity of 79.6%. A second model, including total reaction time, correctly classified 83.2% of cases and resulted in sensitivity and specificity of 84.8% and 83.7%, respectively. DISCUSSION Results supported RIMCAT as an effective digital tool for cognitive screening of older adults in supervised settings.

Background: The increasing prevalence of dementia and mild cognitive impairment (MCI) underscores the need for reliable and scalable digital cognitive screening tools. Although several studies have validated smartphone- or tablet-based assessments in community-dwelling older adults, few have examined their reliability in clinical populations with neurological disorders. This study aimed to evaluate the intra- and inter-rater reliability and agreement between the traditional paper-based Mini-Mental State Examination-Korean version (MMSE-K) and a tablet PC-based MMSE-K kit in patients with neurologic diseases undergoing rehabilitation. Methods: A total of 32 patients with neurological conditions—including stroke-related, encephalitic, and myelopathic disorders—participated in this study. Two occupational therapists (OT-A and OT-B) independently administered both the paper- and tablet-based MMSE-K versions following standardized digital instructions and fixed response rules. The intra- and inter-rater reliabilities of the tablet version were analyzed using intraclass correlation coefficients (ICCs) with a two-week retest interval, while Bland–Altman plots were used to assess agreement between the paper and tablet scores. Results: The tablet-based MMSE-K showed strong agreement with the paper-based version (r = 0.969, 95% CI 0.936–0.985, p = 1.05 × 10−19). Intra- and inter-rater reliabilities were excellent, with ICCs ranging from 0.89 to 0.98 for domain scores and 0.98 for the total score, and the Bland–Altman plots showing acceptable agreement without systematic bias. Minor variability was observed in the Attention/Calculation and Comprehension/Judgment domains. Conclusions: The tablet PC-based MMSE-K kit provides a standardized, examiner-independent, and reliable alternative to the traditional paper version for assessing cognitive function in patients with neurologic diseases. These findings highlight the tool’s potential for clinical deployment in hospital and rehabilitation settings, bridging the gap between traditional paper assessments and automated digital screening. Future multicenter studies with larger, disease-diverse cohorts are warranted to establish normative data and validate its diagnostic precision for broader clinical use.

Conventional neuropsychological screening tools for mild cognitive impairment (MCI) face challenges in terms of accuracy and practicality. Digital health solutions, such as unobtrusively capturing smartphone interaction data, offer a promising alternative. However, the potential of digital biomarkers as a surrogate for MCI screening remains unclear, with few comparisons between smartphone interactions and existing screening tools. This study aimed to investigate the effectiveness of smartphone-derived keystroke dynamics, captured via the Neurokeys keyboard app, in distinguishing patients with MCI from healthy controls (HCs). This study also compared the discriminant performance of these digital biomarkers against the Korean version of the Montreal Cognitive Assessment (MoCA-K), which is widely used for MCI detection in clinical settings. A total of 64 HCs and 47 patients with MCI were recruited. Over a 1-month period, participants generated 3530 typing sessions, with 2740 (77.6%) analyzed for this study. Keystroke metrics, including hold time and flight time, were extracted. Receiver operating characteristics analysis was used to assess the sensitivity and specificity of keystroke dynamics in discriminating between HCs and patients with MCI. This study also explored the correlation between keystroke dynamics and MoCA-K scores. Patients with MCI had significantly higher keystroke latency than HCs (P<.001). In particular, latency between key presses resulted in the highest sensitivity (97.9%) and specificity (96.9%). In addition, keystroke dynamics were significantly correlated with the MoCA-K (hold time: r=-.468; P<.001; flight time: r=-.497; P<.001), further supporting the validity of these digital biomarkers. These findings highlight the potential of smartphone-derived keystroke dynamics as an effective and ecologically valid tool for screening MCI. With higher sensitivity and specificity than the MoCA-K, particularly in measuring flight time, keystroke dynamics can serve as a noninvasive, scalable, and continuous method for early cognitive impairment detection. This novel approach could revolutionize MCI screening, offering a practical alternative to traditional tools in everyday settings. Thai Clinical Trials Registry TCTR20220415002; https://www.thaiclinicaltrials.org/show/TCTR20220415002.

The rising prevalence of dementia necessitates a scalable solution to cognitive screening. Paper-based cognitive screening examinations are well-validated but minimally scalable. If a digital cognitive screening examination could replicate paper-based screening, it may improve scalability while potentially maintaining the performance of these well-validated paper-based tests. Here, we evaluate the Rapid Online Cognitive Assessment (RoCA), a remote and self-administered digital cognitive screening examination. The objective of this study was to validate the ability of RoCA to reliably evaluate patient input, identify patients with cognitive impairment relative to the established tests, and evaluate its potential as a screening tool. RoCA uses a convolutional neural network to evaluate a patient's ability to perform common cognitive screening tasks: wireframe diagram copying and clock drawing tests. To evaluate RoCA, we compared its evaluations with those of established paper-based tests. This open-label study consists of 46 patients (age range 33-82 years) who were enrolled from neurology clinics. Patients completed the RoCA screening examination and either Addenbrooke's Cognitive Examination-3 (ACE-3, n=35) or Montreal Cognitive Assessment (MoCA, n=11). We evaluated 3 primary metrics of RoCA's performance: (1) ability to correctly evaluate patient inputs, (2) ability to identify patients with cognitive impairment compared to ACE-3 and MoCA, and (3) performance as a screening tool. RoCA classifies patients similarly to gold standard paper-based tests, with a receiver operating characteristic area under the curve of 0.81 (95% CI 0.67-0.91; P<.001). RoCA achieved sensitivity of 0.94 (95% CI 0.80-1.0; P<.001). This was robust to multiple control analyses. Approximately 83% (16/19) of the patient respondents reported RoCA as highly intuitive, with 95% (18/19) perceiving it as adding value to their care. RoCA may act as a simple and highly scalable digital cognitive screening examination. However, due to the limitations of this study, further work is required to evaluate the ability of RoCA to be generalizable across patient populations, assess its performance in an entirely remote manner, and analyze the effect of digital literacy.

Depression, the most prevalent mental illness, is underdiagnosed and undertreated, highlighting the need to extend the scope of current screening methods. Here, we use language from Facebook posts of consenting individuals to predict depression recorded in electronic medical records. We accessed the history of Facebook statuses posted by 683 patients visiting a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical records. Using only the language preceding their first documentation of a diagnosis of depression, we could identify depressed patients with fair accuracy [area under the curve (AUC) = 0.69], approximately matching the accuracy of screening surveys benchmarked against medical records. Restricting Facebook data to only the 6 months immediately preceding the first documented diagnosis of depression yielded a higher prediction accuracy (AUC = 0.72) for those users who had sufficient Facebook data. Significant prediction of future depression status was possible as far as 3 months before its first documentation. We found that language predictors of depression include emotional (sadness), interpersonal (loneliness, hostility), and cognitive (preoccupation with the self, rumination) processes. Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures.

Background Disease-modifying treatments for Alzheimer’s disease highlight the need for early detection of cognitive decline. However, at present, most primary care providers do not perform routine cognitive testing, in part due to a lack of access to practical cognitive assessments, as well as time and resources to administer and interpret the tests. Brief and sensitive digital cognitive assessments, such as the Digital Clock and Recall (DCR™), have the potential to address this need. Here, we examine the advantages of DCR over the Mini-Mental State Examination (MMSE) in detecting mild cognitive impairment (MCI) and mild dementia. Methods We studied 706 participants from the multisite Bio-Hermes study (age mean ± SD = 71.5 ± 6.7; 58.9% female; years of education mean ± SD = 15.4 ± 2.7; primary language English), classified as cognitively unimpaired (CU; n  = 360), mild cognitive impairment (MCI; n  = 234), or probable mild Alzheimer’s dementia (pAD; n  = 111) based on a review of medical history with selected cognitive and imaging tests. We evaluated cognitive classifications (MCI and early dementia) based on the DCR and the MMSE against cohorts based on the results of the Rey Auditory Verbal Learning Test (RAVLT), the Trail Making Test-Part B (TMT-B), and the Functional Activities Questionnaire (FAQ). We also compared the influence of demographic variables such as race (White vs. Non-White), ethnicity (Hispanic vs. Non-Hispanic), and level of education (≥ 15 years vs. < 15 years) on the DCR and MMSE scores. Results The DCR was superior on average to the MMSE in classifying mild cognitive impairment and early dementia, AUC = 0.70 for the DCR vs. 0.63 for the MMSE. DCR administration was also significantly faster (completed in less than 3 min regardless of cognitive status and age). Among 104 individuals who were labeled as “cognitively unimpaired” by the MMSE (score ≥ 28) but actually had verbal memory impairment as confirmed by the RAVLT, the DCR identified 84 (80.7%) as impaired. Moreover, the DCR score was significantly less biased by ethnicity than the MMSE, with no significant difference in the DCR score between Hispanic and non-Hispanic individuals. Conclusions DCR outperforms the MMSE in detecting and classifying cognitive impairment—in a fraction of the time—while being not influenced by a patient’s ethnicity. The results support the utility of DCR as a sensitive and efficient cognitive assessment in primary care settings. Trial registration ClinicalTrials.gov identifier NCT04733989.

Recent advancements in cognitive neuroscience and digital technology have significantly accelerated the adoption of digital therapeutics for cognitive impairment. This viewpoint explores the innovative applications of digital therapeutics in the assessment, intervention, management, and monitoring of cognitive disorders while highlighting key challenges that impede their widespread integration into clinical practice. Drawing on the definition of cognitive digital therapeutics (CDTx) and the multistakeholder collaboration required for its development and implementation, this paper examines the role of digital technologies in cognitive health and explores challenges from multiple perspectives, including clinical practice, policy framework, user adoption, ethics and privacy, and data interoperability and system integration. In addition, this viewpoint offers strategic recommendations to address the challenges and future prospects of CDTx, emphasizing the importance of multistakeholder collaboration, prioritizing user-centered design, and leveraging emerging technologies such as artificial intelligence to enhance the scalability, sustainability, and future integration of CDTx.

INTRODUCTION Vascular cognitive impairment (VCI), a major cause of cognitive impairment, remains underdiagnosed due to varying non‐amnestic manifestations. It is important to detect VCI at the mild cognitive impairment (MCI) stage or earlier. We aimed to develop and validate ReCOGnAIze, a tablet‐based, gamified, and interpretable app to detect VCI and MCI. METHODS A multi‐phase, cross sectional study in an Asian community cohort with development phase (n = 200) and validation with 235 independent participants having comprehensive neuroimaging and neuropsychological data. RESULTS In differentiating VCI, ReCOGnAIze achieved strong performance (n = 154, AUC = 0.85), identifying digital features: processing speed and response time variability, consistent with known VCI impairments of executive functioning. Additionally, a generalizable ReCOGnAIze composite score distinguished MCI from cognitively healthy (CH) (n = 235, AUC = 0.90), outperforming the Montreal Cognitive Assessment (MoCA) (AUC = 0.70). DISCUSSION ReCOGnAIze is a scalable, explainable artificial intelligence (AI) tool that accurately detects VCI and MCI, with gamified, tablet‐based, interpretable tasks. Highlights Non‐significant differences on Montreal Cognitive Assessment (MoCA) for vascular cognitive impairment (VCI). ReCOGnAIze artificial intelligence (AI) models identify VCI with area under the curve (AUC) of 0.85. ReCOGnAIze games detect mild cognitive impairment (MCI) with AUC of 0.90, outperforming MoCA (AUC = 0.7). Processing speed and response time variability are key VCI markers.

Dementia has become a major public health concern due to its heavy disease burden. Mild cognitive impairment (MCI) is a transitional stage between healthy aging and dementia. Early identification of MCI is an essential step in dementia prevention. Based on machine learning (ML) methods, this study aimed to develop and validate a stable and scalable panel of cognitive tests for the early detection of MCI and dementia based on the Chinese Neuropsychological Consensus Battery (CNCB) in the Chinese Neuropsychological Normative Project (CN-NORM) cohort. CN-NORM was a nationwide, multicenter study conducted in China with 871 participants, including an MCI group (n=327, 37.5%), a dementia group (n=186, 21.4%), and a cognitively normal (CN) group (n=358, 41.1%). We used the following 4 algorithms to select candidate variables: the F-score according to the SelectKBest method, the area under the curve (AUC) from logistic regression (LR), P values from the logit method, and backward stepwise elimination. Different models were constructed after considering the administration duration and complexity of combinations of various tests. Receiver operating characteristic curve and AUC metrics were used to evaluate the discriminative ability of the models via stratified sampling cross-validation and LR and support vector classification (SVC) algorithms. This model was further validated in the Alzheimer's Disease Neuroimaging Initiative phase 3 (ADNI-3) cohort (N=743), which included 416 (56%) CN subjects, 237 (31.9%) patients with MCI, and 90 (12.1%) patients with dementia. Except for social cognition, all other domains in the CNCB differed between the MCI and CN groups (P<.008). In feature selection results regarding discrimination between the MCI and CN groups, the Hopkins Verbal Learning Test-5 minutes Recall had the best performance, with the highest mean AUC of up to 0.80 (SD 0.02) and an F-score of up to 258.70. The scalability of model 5 (Hopkins Verbal Learning Test-5 minutes Recall and Trail Making Test-B) was the lowest. Model 5 achieved a higher level of discrimination than the Hong Kong Brief Cognitive test score in distinguishing between the MCI and CN groups (P<.05). Model 5 also provided the highest sensitivity of up to 0.82 (range 0.72-0.92) and 0.83 (range 0.75-0.91) according to LR and SVC, respectively. This model yielded a similar robust discriminative performance in the ADNI-3 cohort regarding differentiation between the MCI and CN groups, with a mean AUC of up to 0.81 (SD 0) according to both LR and SVC algorithms. We developed a stable and scalable composite neurocognitive test based on ML that could differentiate not only between patients with MCI and controls but also between patients with different stages of cognitive impairment. This composite neurocognitive test is a feasible and practical digital biomarker that can potentially be used in large-scale cognitive screening and intervention studies.