This review paper explores recent advances in machine learning approaches with emphasis on deep learning techniques for non-invasive cognitive impairment detection.We examine various non-invasive indicators of cognitive decline, including speech and language, facial, and motoric mobility. The paper provides an overview of relevant datasets, feature-extracting techniques, and deep-learning architectures applied to this domain. The paper comprises 60 peer-reviewed papers that mainly utilize deep learning models to detect cognitive impairment conditions. We have analyzed the performance of different methods across modalities and observed that speech and language-based methods generally achieved the highest detection performance. Studies combining acoustic and linguistic features tended to outperform those using a single modality. Facial analysis methods showed promise for visual modalities but were less extensively studied. Most papers focused on binary classification (impaired vs. non-impaired), with fewer addressing multi-class or regression tasks. Transfer learning and pre-trained language models emerged as popular and effective techniques, especially for linguistic analysis. Despite significant progress, several challenges remain, including data standardization and accessibility, model explainability, longitudinal analysis limitations, and clinical adaptation. Lastly, we propose future research directions, such as investigating language-agnostic speech analysis methods, developing multi-modal diagnostic systems, and addressing ethical considerations in AI-assisted healthcare. By synthesizing current trends and identifying key obstacles, this review aims to guide further development of deep learning-based cognitive impairment detection systems to improve early diagnosis and ultimately patient outcomes.

Timely recognition of intraoperative bleeding can support visualization and hemostatic response. This narrative review organizes representative systems by temporal purpose - anticipation, detection, and post-hoc assessment - while treating input sources separately. Video classifiers, object detectors, segmentation-assisted models, and synchronized device - video pipelines demonstrate distinct capabilities, but their reported metrics are not interchangeable. One device-associated detector achieved 81.0% sensitivity with 4.6% precision, corresponding to roughly eight false alerts per operation, whereas other studies evaluated event counts, duration, or warning lead time. We distinguish offline testing, streaming feasibility, and prospective intraoperative evaluation, and relate these evidence levels to event-level accuracy, alert burden, and response time. Current evidence remains limited by small datasets, uncertain transfer across procedures and institutions, workflow integration, and surgeon interaction. Future studies should test transferable contextual cues and determine prospectively whether alerts improve recognition or hemostatic response without excessive interruption.

Background: Biological aging varies substantially among individuals of the same chronological age, yet biological aging biomarkers require biospecimen collection and laboratory processing, limiting scalability for repeated monitoring. Objective: This study evaluated whether consumer smartwatch-derived digital biomarkers are associated with the pace of biological aging, quantified using DunedinPACE, a biomarker that measures biological aging rates. Methods: Baseline data were analyzed from the Healthy Minds for Life study within the Precision Aging Network. Among 1305 enrolled participants, 621 community-dwelling adults aged 50–79 years had both DunedinPACE and smartwatch data available. Smartwatch-derived biomarkers were grouped into mobility, cardiorespiratory physiology, sleep, and physiological stress domains. Associations with DunedinPACE were examined using Spearman correlations and adjusted linear regression models. Results: Significant associations were observed across all four domains. Daily step count showed the strongest mobility-domain association with DunedinPACE (r = −0.25, p < 0.01). Mean sleep peripheral oxygen saturation showed the strongest sleep-domain association (r = −0.22, p < 0.01). Resting heart rate showed the strongest physiological stress-domain association (r = 0.24, p < 0.01). Adjusted regression models supported these findings, with lower daily mobility, poorer sleep oxygenation and efficiency, greater wake after sleep onset, and higher resting heart rate showing the most consistent associations with higher DunedinPACE values. Conclusions: Smartwatch-derived biomarkers captured a multidomain profile of accelerated biological aging characterized by lower mobility, poorer sleep health, and elevated physiological stress. Consumer wearables can provide low-burden, real-world indicators of biological vulnerability, although longitudinal studies are needed before clinical use.