A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

This facilitates something we covered earlier, remote patient monitoring, allowing healthcare providers to track vital signs, manage chronic conditions, and intervene proactively. In this review, we explore the current applications of AI in healthcare, focusing on key areas such as administrative processes, medical imaging, diagnostics, and surgical interventions. We also https://factswanted.net/what-are-wearable-tech-health-benefits/ examine the challenges that must be overcome to fully realize the potential of AI in this domain and discuss the future frontiers of smart healthcare systems.

Support

With hundreds of layers and millions of parameters, DNNs are often viewed as black-box models (Castelvecchi 2016; Albahri et al. 2024). As black-box AI models are increasingly used for critical predictions, the demand for transparency from AI stakeholders is growing (Kalasampath et al. 2025). The concern is that these AI models may produce decisions that cannot be justified or explained in detail (Alammar et al. 2023). Therefore, clear explanations are essential, especially in fields such as precision medicine, where experts require more information than just a binary prediction to support their diagnoses (Mienye et al. 2024). Generally, people are hesitant to adopt methods that are not easily interpretable and trustworthy, particularly with the rising demand for ethical AI (Arrieta et al. 2020; Alzubaidi et al. 2023).

IoT and Wearable Health Devices

Acknowledging and addressing the digital divide through constant reconsideration is imperative in order to enhance equity in accessing telehealth services 67. A smart health ecosystem should seamlessly integrate technologies into people’s everyday lives and routines; this is likely to lead to greater adoption and engagement. Theories such as normalization process theory 58, or social practice theory 59,60, provide lenses through which we can understand how interventions might build on, integrate with, or disrupt everyday life. Implementation approaches must prevent digital exclusion and inequities in health care access 61,62.

Understanding the core of smart healthcare systems

The paper in Alshamrani (2022) discussed how AI and ML can be used for remote healthcare monitoring to collect and evaluate a variety of information, which in turn informs clinical decision support systems and the provision of healthcare services. Despite significant progress in AI within the healthcare system, it may face considerable challenges, especially in gaining user trust and creating effective training datasets. To be specific, traditional ML requires all training data to be gathered in a centralized place. This centralized data indeed raises privacy and security issues and can be computationally demanding for training.

The work in Ding et al. (2020) proposed a Deep Learning-Based image encryption and decryption network (DeepEDN) to facilitate the encryption and decryption of medical images. The work in Manimurugan et al. (2020) proposed a DL-based method and Deep Belief Network (DBN) to detect network intrusion in the IoT healthcare system. Federated learning can also be used for data security and privacy in healthcare systems (Hossen et al. 2022). The work in Mothukuri et al. (2021) proposed a federated learning-based anomaly detection method to proactively detect intrusions in IoT networks by utilizing decentralized on-device data. Fog/edge computing addresses the challenges posed by network latency and the need for fast response times. By processing data locally on IoT devices, fog/edge computing minimizes the time required for data to travel back and forth between the device and a distant cloud server.

• Addressing this heterogeneity in data is crucial for designing federated learning frameworks that can efficiently train models and improve performance in IoT-based healthcare applications.
• Additionally, ethical concerns related to accountability and bias need to be addressed before AI can be fully trusted for diagnostic decision-making 51.
• Their approach combines correlation-based and sequential feature selection techniques with a cascaded LSTM and Naive Bayes classifier to enhance threat detection accuracy.
• Smart healthcare systems are patient-centered and make use of Smart Healthcare System (SHS) devices for remote monitoring of patients.
• Theories such as normalization process theory 58, or social practice theory 59,60, provide lenses through which we can understand how interventions might build on, integrate with, or disrupt everyday life.

Blockchain for Health Data Security

Quantum communication and computing can improve the efficiency of computing and provide strong security for future 6 G networks (Zhang et al. 2019). Quantum communications offer robust security by utilizing a quantum key based on the quantum no-cloning theorem and the uncertainty principle. When eavesdroppers attempt to observe, measure, or copy data in quantum communications, the quantum state is disturbed, making the eavesdropping behavior easily detectable. This inherent property ensures the integrity and confidentiality of the transmitted information. Quantum key distribution ensures that sensitive medical information, such as EHRs and diagnostic data, is protected against eavesdropping and cyberattacks. This technology could facilitate secure remote consultations, real-time monitoring, and the safe sharing of critical patient data between healthcare providers.

This interconnected procedure enables healthcare professionals to make timely, informed decisions. Precisely, IoT-driven healthcare minimizes human error by networking all devices to a decision support system, hence empowering physicians to deliver more precise diagnoses. More specifically, it has the potential to revolutionize the healthcare system by providing ultra-secure data transmission, enhancing patient privacy, and improving the reliability of telemedicine.

2 Current challenges in sensors and smart devices and future research directions

The paper emphasized the requirement for multidisciplinary collaboration to create effective tools. The work in Vora et al. (2023) discussed how https://www.yaldex.com/javascript-tutorial-4/pg_0072.htm AI and ML transform drug discovery and pharmaceutical development. The paper demonstrated that by analyzing extensive biological data, AI helps identify disease targets and predict interactions with potential drug candidates, increasing the chances of successful drug approvals.

The ability to track these metrics outside of clinical settings allows for more comprehensive and proactive management of chronic diseases. Patients can share this data seamlessly with their healthcare providers, ensuring timely interventions whenever anomalies are detected, thus mitigating risks before they escalate. Edge devices typically have less processing power, memory, and storage compared to the cloud servers. Edge devices often run on batteries, so energy-efficient models are crucial to avoid the rapid depletion of power resources. Therefore, developing applications that function seamlessly across a wide range of devices becomes increasingly important- an area that may be worth further exploration in future research.