Jeong-ah Kim, Muhammad Ashad Kabir, Daniel Terry +1cs.AI
Introduction: Medication errors (MEs) represent a significant threat to global healthcare systems, contributing to patient harm. Introducing artificial intelligence (AI) in rural healthcare enhances patient safety. The aim is to explore the applications and effectiveness of AI technologies in enhancing patient safety and reducing medication errors in rural health settings. Methods: A scoping review was conducted through a systematic literature search spanning 2012 to 2025 across multiple databases, including EBSCohost, Emcare (Ovid), MEDLINE, and the ProQuest Consumer Health Database. Twelve primary studies from nine different nations were examined. Data were analysed thematically to obtain insights on AI interventions across the medication process. Results: AI technologies have been integrated into every stage of medication management, right from prescribing and dispensing to administration and post-administration monitoring. Four key themes came to light: (1) the various types of AI being utilised (like Clinical Decision Support Systems, Machine Learning, Natural Language Processing, and smart pumps); (2) the phases of the medication process that are affected; (3) how effective these technologies are in minimising errors and boosting workflow safety; and (4) rural-specific challenges including infrastructure, staff training, system integration, and alert fatigue. Several studies have demonstrated that machine learning-based surveillance improves incident detection and reduces prescribing and transcription errors by an impressive 34% to 80%. Barriers included lack of governance frameworks, financial limitations, and clinician resistance, which still present major obstacles. Conclusion: In rural healthcare, AI technologies hold great potential for enhancing pharmaceutical safety. They can allow data-driven monitoring, automate processes, and offer clinical decision assistance.
Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain heterogeneous. We conducted a PRISMAScR scoping review of peer-reviewed studies published between January 2017 and March 2026 describing foundation models trained exclusively on radiological imaging data. Sixty-seven studies were included and mapped across three pillars: data scale and heterogeneity, architectural and pretraining scalability, and downstream transferability and generalization. Datasets primarily covered brain MRI, thoracoabdominal CT, and chest X-ray, ranging from fewer than 100,000 samples to multi-million-image cohorts. Transformer-based architectures and self-supervised pretraining predominated, particularly masked image modeling, contrastive learning and multi-stage approaches. Evaluation focused mainly on segmentation and classification, whereas cross-center, cross-scanner, anatomical and modality-shift validation was inconsistently reported. Alignment with FUTURE-AI principles was uneven. Overall, radiology-specific VFMs show promising transferability, but clinical translation remains constrained by limited data representativeness, heterogeneous benchmarks, incomplete reporting and insufficient deployment-oriented evaluation.
Brain-computer interfaces (BCIs) can restore sensory and motor function in individuals with severe neurological impairment, but the literature is fragmented between invasive neuroprosthetics and non-invasive electrophysiological decoders, with inconsistent terminology and metrics. This scoping review maps BCI-mediated sensory restoration along a unified 2x2 framework (invasiveness x signal direction), charts representative modalities and their trade-offs, and synthesizes a convergence roadmap for the field. Eligible sources were peer-reviewed studies, clinical trials, and authoritative reviews on BCI or neuroprosthetic systems for sensory or motor restoration, substitution, or augmentation, published in English between 1969 and 2025, restricted to high-impact venues to prioritize landmark evidence. Rather than an exhaustive database search, we charted a purposively assembled, citation-chained corpus of 31 pivotal sources for modality, signal type, invasiveness, signal direction, resolution, clinical risk, cost, and regulatory maturity. We define and distinguish restoration, substitution, and augmentation, and map the corpus onto the four quadrants of the framework. The corpus is dominated by efferent restoration (21 of 31) and invasive interfaces (22 of 31), and is concentrated after 2015 (25 of 31). Non-invasive, AI-augmented silent-speech decoding has matured rapidly since 2023, while invasive speech and motor neuroprostheses have achieved near-conversational communication rates. The unified taxonomy clarifies trade-offs between pathways and the role of foundation models in closing the gap between them. We outline a near-, medium-, and long-term roadmap toward closed-loop, bidirectional restoration, and identify gaps in metric standardization, longitudinal evidence, and cross-community collaboration as priorities for future research.