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The integration of artificial intelligence (AI) into healthcare systems marks a fundamental shift from isolated predictive analytics tools to embedded, scalable architectures that support autonomous governance. This narrative review synthesizes 28 peer-reviewed publications from leading journals to examine AI’s role across healthcare infrastructure and clinical analytics. Early work established deep learning foundations for risk prediction, diagnostic support, and prognostic modelling using multimodal data. These capabilities rapidly evolved into system-level applications that enhance data ingestion, real-time inference, and operational optimisation across entire care ecosystems.By the early 2020s, attention turned to deployment realities, including clinician acceptance, cost-effectiveness, and integration into existing workflows. Frameworks for responsible implementation emerged alongside regulatory perspectives that emphasise safety, equity, and continuous oversight. Recent contributions highlight the transition toward closed-loop systems in which predictive outputs inform decisions, trigger interventions, and feed outcome data back for model recalibration. Governance architectures now address ethical challenges, explainability gaps, and the move from generalist to specialised medical AI.This review organises the literature through an original systems-level lens spanning four interconnected pillars—data foundations, analytic intelligence, deployment mechanisms, and governance layers—rather than replicating prior application-specific taxonomies. Cross-study analysis reveals consistent patterns: predictive analytics serve as the foundational engine, clinical decision support acts as the execution layer, closed-loop feedback enables adaptation, and governance ensures sustainable autonomy. The synthesis demonstrates that AI is no longer an adjunct technology but a core infrastructural element reshaping how healthcare systems ingest, process, act upon, and learn from data at scale.Trajectory as a coherent progression toward autonomous yet human-centred governance, the review provides clinicians, system architects, and policymakers with a unified understanding of current capabilities and the infrastructural requirements for responsible scaling.

Synthetic electronic health record (EHR) data generation has emerged as a potential solution to balancing clinical data accessibility with patient privacy, using generative artificial intelligence to simulate tabular, longitudinal, and textual health records without exposing identifiable patient information. This critical review, informed by PRISMA-ScR methodology, examines studies published between 2017 and 2025 focusing on generative models for synthetic EHR creation, with particular attention to privacy risks, data fidelity, downstream task utility, and ethical or regulatory considerations. A total of 67 studies were included after systematic screening, showing a dominance of GAN-based approaches alongside growing use of diffusion models and large language models in recent years, although privacy assessment and benchmarking practices remain inconsistent. Overall, the evidence suggests that while synthetic EHR data can facilitate data sharing, research, and model development, achieving a balance between realism, utility, and privacy remains challenging, as high statistical fidelity does not necessarily translate into clinical usefulness and strong downstream performance does not ensure adequate privacy protection.

Sleep disorders, including obstructive sleep apnea, insomnia, restless legs syndrome, narcolepsy, and central sleep apnea, represent a major public health burden. Polysomnography is the diagnostic gold standard but is resource-intensive, leading to increasing use of home sleep apnea testing and wearable devices to improve accessibility. This systematic review evaluates deep learning models in sleep medicine across polysomnography, home sleep apnea testing, and wearable data, focusing on architectures, signal types, validation approaches, diagnostic tasks, and clinical readiness. A PRISMA 2020–compliant search was conducted in PubMed, IEEE Xplore, Scopus, and Web of Science for studies published from 2017 to 2025, including those applying deep learning for sleep staging, apnea/hypopnea detection, or sleep disorder diagnosis using PSG, HSAT, or wearable-derived signals. Twenty-nine studies were included. Convolutional neural networks were the most widely used architecture, often combined with recurrent or hybrid models for temporal dependencies, while transformer-based models have recently emerged for long-sequence sleep analysis. Deep learning methods demonstrate strong performance in sleep staging and respiratory event detection, especially using polysomnography data. However, limited external validation, heterogeneous datasets, and a lack of prospective clinical deployment remain major barriers to clinical translation.

Generative artificial intelligence (AI), including GANs, VAEs, and diffusion models, is increasingly used for synthesizing and enhancing medical images, helping address challenges such as limited data, expensive acquisition, and rare disease representation. This systematic review examines studies on generative AI methods for MRI, CT, X-ray, and pathology image synthesis from 2017 to 2026, focusing on synthesis tasks, evaluation strategies, and clinical utility. A PRISMA 2020-compliant search of PubMed, IEEE Xplore, Scopus, and Web of Science identified peer-reviewed research on generative models for medical image synthesis, augmentation, harmonization, or cross-modality translation. Findings show a shift from GAN-based methods to diffusion models post-2022, with MRI and CT studies emphasizing cross-modality translation, and X-ray and pathology studies focusing on augmentation and diagnostic utility. Despite GANs' continued dominance, diffusion models are gaining traction for improving image fidelity and diversity. However, evaluation practices remain inconsistent, with limited inclusion of clinically relevant assessments. This review follows PRISMA 2020 guidelines and provides a narrative synthesis of the evidence.

This systematic review examines the use of edge artificial intelligence (AI) and wearable sensors for real-time patient monitoring in smart hospitals and home settings, focusing on detecting deterioration, falls, arrhythmias, and infection-related changes. The review synthesizes studies from 2017 to 2026 on edge AI architectures, wearable sensor fusion, and clinical alert systems, emphasizing latency, power constraints, alert performance, and integration into clinical workflows. A PRISMA 2020-compliant search identified 127 studies from 2,100 records, with findings showing that while edge AI execution grew post-2020, it still represented a minority of designs. Sensor fusion was often linked to broader event coverage but increased implementation complexity. The review concludes that edge AI can reduce latency and enhance privacy but introduces challenges related to power usage, model complexity, device reliability, and maintenance, with limited clinical validation of alert systems and few studies addressing alert fatigue or clinician response.