Artificial Intelligence (AI) is poised to revolutionize numerous aspects of human life, with healthcare among the most critical fields set to benefit from this transformation. Medicine remains one of the most challenging, expensive, and impactful sectors, with challenges https://www.mrosidin.com/national-institutes-of-health-nih-turning-discovery-into-health.html such as information retrieval, data organization, diagnostic accuracy, and cost reduction. AI is uniquely suited to address these challenges, ultimately improving the quality of life and reducing healthcare costs for patients worldwide.

3.1 Systematic literature review papers

Overcoming these obstacles will require more affordable technologies and stronger regulatory frameworks 110. AI could also be used to develop predictive models that identify patients at risk of developing chronic conditions, such as diabetes or cardiovascular disease. By analyzing genetic information, lifestyle factors, and clinical data, AI could provide personalized recommendations for preventing the onset of these conditions 75. By analyzing a patient’s genetic information, medical history, and clinical data, AI can provide tailored diagnostic recommendations and treatment plans 73.

MEDICALCHAIN

To this end, the paper discusses the application areas of this architecture for mHealth, AAL, e-health, implants, early warning systems, and population monitoring in smart cities. The work in Negra et al. (2016) demonstrated that the RPM system can be implemented in real-time with multi-tier pervasive systems based on the use of WBAN. Smart healthcare systems are patient-centered and make use of Smart Healthcare System (SHS) devices for remote monitoring of patients. These systems are supported by networks established through sensors and other connected devices, which collect data related to both patients and health organizations. This data is then utilized for various tasks such as generating patient records, planning treatment, disease detection, and sensing patient conditions13. Recent studies highlight AI’s potential in enhancing post-operative evaluations and implant selection in spinal surgeries.

Innovative IoT-Based Healthcare Devices: A New Era of Patient Monitoring and Care

For detecting and classifying cancer deep neural networks (DNN) and Naive Bayes classifiers are utilized. The key concept of the proposed system is to generate real-time alarms in emergency conditions (registered patients of the proposed system), and the cloud is employed to store the patient data. Mohamad et al. 23 developed an android system to pronounce and write Hijaiyah letters using a dynamic time-wrapping approach.

• In this regard, the work in Sodhro et al. (2021) investigated a smart healthcare framework based on the IoMT, emphasizing the integration of IoT technologies with medical services.
• For instance, AI has been used to detect diabetic retinopathy from retinal images with high accuracy, allowing for early intervention and preventing vision loss 67.
• NFV involves transforming network functions like routing, load balancing, and firewalls into virtualizing networks through software applications.
• Federated learning addresses the challenges of centralized data collection and processing in healthcare by enabling distributed collaborative ML.
• Managing user logs with episodic memory can enhance user comfort and encourage prolonged use of the metaverse.

ATD addresses these issues by utilizing adaptive ensemble techniques for bias mitigation, along with hybrid encryption and authentication mechanisms, to ensure secure and efficient model exchange. Consequently, ATD achieves faster convergence, enhanced data security, and equitable model contributions, establishing it as a practical and innovative solution for decentralized AI in real-world, multi-institutional environments. AI-To-Data (ATD) is a next-generation decentralized learning framework designed to tackle key limitations in current collaborative AI models (Alzubaidi et al. 2025) (see Fig. 16). Unlike traditional approaches that rely on central servers or aggregators, ATD facilitates fully connected, peer-to-peer weight sharing between different institutions without requiring the sharing of raw data. This framework supports multi-modal learning (e.g., audio, image, video, and text) and features bias-aware model aggregation to ensure fairness across nodes with diverse data.

The content and devices within the system may also be tailored depending on, for example, a person’s experience with technology, diagnosis, personality, and physical and cognitive capabilities. Smart tech and digital transformation in healthcare are paving the way into the future and flipping the whole game on its head. Blockchain technology introduces a layer of security and transparency to healthcare data management in smart hospitals.

The central server consolidates these updates to refine a global model, which is subsequently redistributed to the devices. Developing XAI is crucial for bridging the gap between complex model predictions and clinical trust. To mitigate biases in AI algorithms, mechanisms such as diverse training datasets, bias detection audits, and fairness-aware modeling can be https://dallasrentapart.com/we-will-not-have-time-to-look-back-how-winter.html employed to address these issues.