Jul 2026· International Journal of Medical and Health Research· Vol 4, pp. 01-14· 0 citations· 45 references
TL;DR
A future where evolving regulation frameworks, such as risk-based control strategies, as well as increased explainability, interpretability, infrastructure protection, and compliance will help to facilitate widespread AI adoption in medicine is identified.
Abstract
Artificial intelligence (AI) has transformed the modern medical landscape. Medical devices benefit from innovative data collection, utilization of machine learning, predictive analytics, and intelligent decision support systems in their design and implementation. Applications include medical devices for diagnosis, screening, patient monitoring, tailored care, robotic procedures, and streamlining hospital processes. Despite these benefits, the development of new AI-powered devices presents many new regulatory, legal, ethical and cyber security challenges to be addressed. This research paper provides a review of the recent literature regarding advances in AI-powered medical devices, including their mechanisms of action, clinical applications, regulation, and regulation challenges. A qualitative narrative review was performed synthesizing the content of 15 carefully selected papers that fit the inclusion criteria. An analysis revealed that AI-powered medical devices have improved accuracy in diagnosis, enabled precision care, advanced patient monitoring, and optimized component manufacturing. Our review further identified a future where evolving regulation frameworks, such as risk-based control strategies, as well as increased explainability, interpretability, infrastructure protection, and compliance will help to facilitate widespread AI adoption in medicine.
The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care, but challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation.
Unknown authors· Journal of Cognitive Human-C...· 0 citations
In the healthcare industry, AI and computer-based systems promise to advance patient care, improve diagnostic precision, and facilitate improved clinical decision-making. In this review, the use of cutting-edge AI tools like machine learning, deep learning, and natural language processing, along with telemedicine systems, clinical decision support systems, electronic health records, and lab automation systems, is explored. Early identification of disease, optimized treatment, evidence-based processes, real-time analytics, and efficient data management can be performed using artificial intelligence techniques. Currently, medical imaging, early diagnosis, drug development, personalized medicine, robotic-assisted surgery, and predictive analytics are important applications. Combining the technologies of Artificial Intelligence, Cloud Computing, and Internet of Medical Things allows for intelligent service in healthcare and constant monitoring of patients. It still, however, has challenges to address, such as the lack of skilled professionals, the cost of implementation, privacy and security issues, connectivity, and data quality. There's also an ethical concern because there's a lack of transparency and algorithmic prejudice. These challenges must be tackled by bridging the gap between disciplines, implementing robust regulations, and continually advancing technology to ensure the successful application of AI in healthcare. The technology could revolutionize the industry.
Fawaz H. Alanazi· Journal of Intelligent Decis...· 0 citations
This paper summarizes recent advances in biosensor technologies, AI-derived biomarkers, and predictive frameworks used to help identify patients more accurately, monitor their progress continuously, and receive optimized therapies.
S. Bukke, Chandrashekar Thalluri, Mallikarjun Vasam et al.· Personalized Medicine· 0 citations
The innovations in mechanical engineering, artificial intelligence, sensing, robotics, Internet of Things (IoT) and healthcare information systems are rapidly converging to revolutionize the design and management of modern medical devices. In a nutshell, the traditional medical devices have been transformed step by step from passive mechanical devices to intelligent cyber-physical devices that can sense physiological states, process multimodal information, assist the doctor in making clinical decisions, and adjust to the patient's needs. This review explores the latest developments of intelligent medical devices from an interdisciplinary point of view, focusing on mechanical design, artificial intelligence, machine learning, the digital twin, wearable systems, robotics, predictive maintenance, remote healthcare and healthcare management. The review brings together the contributions of mechanical engineering to provide reliable structures, actuation mechanisms, biomedical materials, ergonomic systems, microfluidic technologies, rehabilitation robots, and minimally invasive technologies, and explores how AI can support perception, prediction, personalization, anomaly detection, decision support and autonomous operation. Focus is paid to AI-based medical devices, digital-twin medical equipment management, AI-driven wearable systems, robotic healthcare technologies, and AI-driven medical equipment data-driven maintenance. Challenges with data quality, interoperability, cyber security, explainability, model drift, regulatory compliance, human factors, clinical validation are critically discussed. It is suggested to introduce a conceptual intelligent medical-device ecosystem comprising both physical devices and edge/cloud computing, AI analytics, digital twins, healthcare management platforms, and human oversight. The review recommends that future intelligent healthcare systems should shift from smart devices to trustworthy, interconnected, adaptive and patient-centric cyber-physical ecosystems. The research directions identified open up avenues for creating safer, more explainable, energy efficient, personalised, and clinically deployable intelligent medical technologies.
G. J. Naik, Burla Srinivas, A. Vijendar et al.· International journal of com...· 0 citations
This work connects biomedical signal capture, on-device AI, clinical translation, and digital ethics to delineate a roadmap for frontier AI wearables, and proposed edge-aware transformer-based models, privacy-preserving analytics, and global multimodal sensing advance intelligent, scalable, and fair AI-driven health monitoring systems.
B. Bali, Favanza Iliya Kwaha· Journal of Engineering Advan...· 0 citations
Introduction: The digital transformation of healthcare is accelerating, driven by unprecedented advancements in Artificial Intelligence (AI). From large language models (LLMs) to biomolecular structure prediction, AI is redefining modern diagnostic and therapeutic standards.
Aim: This review evaluates the current state of AI applications in medicine, focusing on clinical knowledge encoding, molecular drug discovery, and administrative workflow optimization, while critically addressing the technical, ethical, and systemic challenges of their institutional implementation.
Materials and Methods: A structured analysis was conducted utilizing a hybrid approach that combines a multi-decade bibliometric trend perspective with a detailed synthesis of 21 landmark publications, clinical trials, and meta-analyses from high-impact journals.
Results: AI demonstrates expert-level performance in medical knowledge retrieval and spatiotemporal diagnostics. AlphaFold 3 has revolutionized computational therapeutics through all-atom biomolecular interaction prediction, while ambient AI scribes significantly reduce physician burnout by automating clinical documentation workflows. However, data-driven "hallucinations" in LLMs and the inherent "black box" nature of deep learning architectures remain critical barriers to autonomous deployment.
Conclusions: AI is successfully transitioning from an isolated research tool into an essential clinical "co-pilot." Achieving its full potential in Medicine 4.0 requires robust frameworks for algorithmic explainability, global dataset diversification, and a strategic synergy between machine precision and human clinical judgment.
Aleksandra Stańczyk, Kinga Haduch, Zuzanna Michalska et al.· International Journal of Inn...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
