Tasks that are routine and repetitive are now completed quickly and accurately without the risk of human error. This allows healthcare staff to focus more on patient care and other critical responsibilities, improving the overall patient experience. In imaging and diagnostics, AI models struggle with data variability and bias, which limits their ability to generalize across different patient populations 109. Additionally, the high costs and training demands of AI-driven surgical systems pose challenges, particularly for hospitals in low-resource settings.
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For future research, it is crucial to develop advanced algorithms that can efficiently handle the challenges of processing heterogeneous, noisy, and inconsistently represented data. This will ensure the effective use of wearable medical sensors in continuous health monitoring, enhancing the reliability and accuracy of healthcare systems. The working of fog computing depends on sensors, controllers, and actuators where sensors are utilized to acquire data, controllers transfer data to actuators or any other devices 79.
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The authors in Jan et al. (2024) proposed a lightweight data fusion approach that is specifically designed for wearable devices in the IoMT to refine medical data transmitted by multiple wearable devices. Power and energy storage technologies must meet diverse requirements for IoT applications, a crucial issue that warrants attention. Energy constraints to date pose a significant challenge for IoT-based healthcare systems, especially for wearable and implantable devices.
To address these challenges, novel distributed learning algorithms are needed for intelligent interfaces in metaverse-enabled wireless systems (Khan et al. 2024). Future work should address the challenges posed by large image sizes, which demand significant memory as both model complexity and pixel count increase. Whole-slide medical images, often containing billions of pixels, frequently exceed the capacity of standard AI tools such as neural networks. Potential solutions include resizing images, which may compromise fine details, or splitting them into smaller patches, which can limit the system’s ability to make holistic connections. Alternatively, identifying and cropping regions of interest can be done manually before inputting them into an AI system, though this introduces a manual step into an otherwise automated process. Developing efficient methods to manage large medical images without losing crucial information will be vital for future advancements.
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The environmental context can also be measured, providing additional opportunities to tailor interventions. This integration enables the development of health care solutions with intelligent prediction capabilities and offers means to provide personalized behavioral support 27,28. The integration of IoT devices will enable continuous monitoring of patients, allowing for real-time data collection and analysis, leading to more personalized and timely medical interventions. AI will play a pivotal role in diagnosing diseases, predicting patient outcomes, and automating routine tasks, freeing up healthcare professionals to focus on more critical patient care activities.
The paper investigated the application-specific sensors, exploring their power systems, on-sensor pre-processing capabilities, and the communication systems they utilize. The paper in Channa et al. (2021) provided SLR that discusses wearables, or on-body sensors, their types and locations, and how AI can be used with eHealth devices. For example, the work in Islam et al. (2015) conducted a review of IoT-based technologies in healthcare systems, discussing the state-of-the-art communication network architectures, services, and applications. The work in Yuehong et al. (2016) provided a comprehensive literature review that summarized the applications of IoT in the healthcare system with a special focus on communication technology.
- The paper in Rahmani et al. (2018) highlighted the strategic role of gateways in IoT-based healthcare systems, particularly in facilitating communication between sensor networks and the Internet.
- The thematic analysis allowed for a synthesis of the findings across multiple studies, providing a comprehensive overview of the state of AI in healthcare and highlighting areas for future research and development.
- NFV can be utilized to deploy and manage network functions, allowing for efficient resource utilization and rapid service deployment.
- Successful AI adoption begins with identifying operational bottlenecks or clinical gaps, then selecting or building tools that directly address those issues.
- AAL encompasses a range of systems, including wearable technology, smart home technologies, and telehealth services, that support everyday tasks such as medication management, fall detection alarms, health monitoring, and emergency response.
By connecting patients and healthcare providers, you can improve your treatment based on continuously collected real-time data. With access to this accurate information, healthcare providers can take immediate action when needed. Therefore, the transformation of the healthcare industry is only happening because patients are empowered by using digital tools at every stage of their journey. Traditional medicine based on biotechnology has gradually started to digitize and offer information that leads to advancements in technology and scientific theory. The idea of intelligent healthcare has increasingly gained attention as information technology has advanced. The Internet of Things (IoT), cloud computing, big data, and artificial intelligence are some of the new information technologies that smart healthcare uses to completely revolutionize conventional medical systems and improve healthcare efficiency.
When applied to large-scale WSNs consisting of hundreds of nodes, the proposed method demonstrates high effectiveness in accurately determining node locations. The work in Mihoubi et al. (2018) introduced an enhanced BAT algorithm, named Dopeffbat, for efficient node localization in WSNs based IoT. By incorporating the Doppler effect to adjust the bats’ velocity, the algorithm enhanced accuracy and convergence speed. Using Euclidean distance as the fitness function, Dopeffbat effectively determined node positions across large-scale deployments. Simulation results confirmed the superior performance of the proposed method compared to the original BAT algorithm and PSO, demonstrating higher precision and faster convergence. The works in Mihoubi et al. (2020); Supreeth and Akhil (2025) presented a hybrid localization method named Enhanced Bat Algorithm with Doppler Effect (EBADE) in large WSN with hundreds of IoT sensors to achieve precise sensor node localization.
The healthcare sector’s commitment to sustainability is evident from the increased investment in green technology and practices that ensure the well-being of the planet while maintaining high standards of patient care. The integration of AI in robotic surgery further improves outcomes by providing real-time data and predictive analytics during procedures. With AI, robotic systems can adapt in real-time, providing surgeons with critical information and suggestions, improving the overall safety and efficacy of the procedures. As technology progresses, the potential for fully autonomous robotic surgeries becomes a tangible possibility, which could be groundbreaking for the future of complex surgical treatments. Virtual health assessments and e-prescriptions are becoming common, providing convenience to patients and reducing the burden on healthcare facilities.
- DL is a subdomain of ML engrossed by algorithms, functioning like the human brain, i.e., neural networks.
- Utilizing a centralized control platform would simplify data traffic management within the network, optimize resource allocation, address the network’s scalability issues, reduce overhead, enhance dependability, and–most importantly–lower latency.
- AI’s transformative potential in healthcare lies in its ability to automate tasks, improve diagnostic accuracy, and optimize resource management.
- It operates as both a network and a protocol to set policies and configure data layers while verifying the sources and destinations of data in real time.
- Additionally, sophisticated data analysis mechanisms are necessary to extract valuable patterns and insights from large datasets.
- Trailblazer case studies from Ochsner Health, Wellstar MCG Health and Cedars-Sinai illustrate how they are applying these approaches.
A Study on AI-Empowered Smart Healthcare: Key Challenges and Opportunities
Input and output layers are known as visible layers, input layers acquire and process the data, whereas the output layer is used to classify or predict the result. In DL algorithms multiple layers and nodes are interlinked together to optimize and enhance the task of either classification or recognition. Layers or algorithms where errors are computed through backward traversal by adjusting nodal weights are known as “Backward Propagation”. These two forward and backward propagations enable the algorithm for claiming the predictions accurately. Going forward, blockchain is expected to continue to play a major role in the healthcare system.
- Following tests, Matilda is diagnosed with type 2 diabetes and is told by her GP that she will need to take insulin as part of her management plan.
- Gain real-time visibility for more efficient, reliable operations from Swisscom powered by Aeris.
- Self-sustaining, or autonomous systems, are devices or networks that function independently without external power or human intervention for an extended period.
- Ismail et al. 12 proposed an efficient solution “a smart speech recognition system” for elderly and disabled patients.
- An effective and robust architecture for heart failure prediction using EHR was investigated in Jin et al. (2018).
- With services like primary care, outpatient infusion, urgent care and more, you can find many of our service lines right in your own community.
6.2 Current challenges in security and privacy techniques and future research directions
The EY Global Consumer Health Survey 2023 finds consumers value access most, but also want cost-effective care and relief from pain and anxiety. And R.P.; resources, S.G.; data curation, N.L.; writing—original draft preparation, N.L., R.P., S.G. And F.D.V.; project administration, M.L.; funding acquisition, M.L., E.D.E. and F.D.V. All authors have read and agreed to the published version of the manuscript.
Most AI diagnostic https://themors.com/where-europes-startups-are-thriving-in-2025/ systems require validation through clinical trials, which can be time-consuming and expensive 91. Additionally, ethical concerns related to accountability and bias need to be addressed before AI can be fully trusted for diagnostic decision-making 51. If the training data are not representative of the entire population, the model may perform poorly for certain demographic groups, leading to disparities in healthcare outcomes 52,53. The integration of Artificial Intelligence (AI) into healthcare represents one of the most transformative trends in modern medicine 1. From administrative tasks to highly complex diagnostics and surgical interventions, AI is making significant strides in reshaping how care is delivered 2.