Background: Epilepsy affects approximately 50 million people globally and imposes a substantial clinical and societal burden, requiring continuous and personalized monitoring for effective management. Wearable artificial intelligence (AI) technologies offer a promising solution by leveraging physiological signals and machine learning for seizure detection and prediction. While various approaches have been proposed, a comprehensive overview summarizing these advances and challenges is still needed. Objective: This review aims to comprehensively explore and map the existing literature on AI-driven wearable technologies for epilepsy, identifying device characteristics, AI methodologies, biosignal measurements, validation approaches, and research gaps. Methods: A scoping review was conducted following the PRISMA-ScR guidelines. A systematic search was performed across six electronic databases (Scopus, MEDLINE, EMBASE, ACM Digital Library, IEEE Xplore, and Google Scholar) to identify relevant studies published up to December 2023. We included studies that developed AI algorithms for epilepsy using non-invasive wearable devices (e.g., smartwatches, smart clothing) and excluded those using non-wearables or in-body devices. Eligible publication types included journal articles, conference papers, and dissertations. Study selection and data extraction were performed independently by six reviewers. The extracted data was synthesized narratively. Results: A total of 68 studies met the inclusion criteria. Research in this domain has increased significantly since 2021, with India, the United States, and China leading contributions. The studies examined both commercial (45.6%) and non-commercial (47.1%) wearable devices, with Empatica smart bands being the most frequently used. The primary biosignals monitored included activity measures (54.4%), cardiovascular metrics (45.6%), brain activity (35.3%), and electrodermal activity (33.8%). The most common AI models were support vector machines (42.6%), random forests (22.1%), and convolutional neural networks (16.2%). Most models focused on seizure detection (77.5%) compared to seizure prediction (22.5%), reflecting a research imbalance that suggests the need for further development in predictive analytics. Sensitivity (80.9%) was the most frequently reported performance metric, indicating a focus on identifying seizures; however, comprehensive clinical validation remains limited. Closed-source data predominated (64.7%), limiting the generalizability of findings. The most used validation methods were leave-one-out cross-validation (30.9%) and k-fold cross-validation (29.4%), while video-EEG served as the primary reference standard (42.6%). Conclusions: Wearable AI technologies show significant promise in epilepsy management, offering real-time, continuous monitoring and early seizure detection. To realize clinical impact, future research should prioritize the standardization of validation methods, promote open data exchange for reproducibility, and develop energy-efficient algorithms that support real-world deployment in wearable devices.

Health care increasingly depends on information and communication technology. This offers both opportunities and challenges when designing connected health systems. While individual studies examined particular cases, there is a limited synthesis of insights across projects. The objective of this paper is to explore these opportunities and challenges by examining 6 diverse connected health projects and synthesizing lessons from an expert workshop. To achieve this, we conducted a full-day workshop that brought together 6 connected health projects. The workshop used an iterative and participatory process which included paper submissions and presentations and facilitated discussions, and a gallery walk to enable cross-case comparison and collaborative reflection. Thematic analysis of workshop outputs was then used to synthesize key opportunities and challenges in designing connected health systems. The 6 projects represented a variety of design methods and approaches to connected health, and their discussion surfaced both opportunities and challenges in this domain. Key opportunities include improving data integration and usability, enhancing collaboration across stakeholders, using a user-centered and iterative design process, addressing complexity in sociotechnical systems, sustainability, and adopting digital infrastructures for seamless communication. Participants also identified important challenges, namely exchange of information, interoperability, and communication; ethical considerations, rules, and regulations; understanding design, evaluation, and standards; actionable data, reliability, quality, and trust in data; and stakeholder involvement. The contribution of this paper lies in the synthesis of insights across multiple projects and perspectives to provide practical guidance for researchers, designers, and policymakers. By highlighting opportunities and challenges in designing connected health systems, the findings emphasize the importance of patient-centered, sustainable, and collaborative design approaches while also pointing to the need to address persistent barriers. Advancing connected health will require adopting iterative and inclusive design processes that prioritize patient-centeredness, sustainability, and collaboration across health care systems.