# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 5 Discussion
One key area of future research is the integration of zero- or few-shot learning methods in healthcare solutions. The former can be beneficial in detecting rare diseases with no preexisting or limited training data, and the latter can be beneficial due to the nature of data collection in healthcare. Another interesting topic is collaborative AI at the edge, where devices at the edge can cooperate and coordinate with each other to improve their capabilities and optimize the workload. Finally, further exploration of distributed AI in resource-limited environments is needed. The strategic allocation and execution of computational tasks across multiple edge nodes plays an important role in improving performance, efficiency, and resource utilization on edge platforms in different use cases, including healthcare. An effective task orchestration can reduce latency, minimize the energy consumption, and optimize the resource availability in the network, providing the required context to perform complex data processing closer to the source of generation. Analyzed solutions integrate a wide range of technologies such as blockchain, which manages the integrity of resource allocation and task execution, and federated learning, which not only ensures that data are kept on edge devices, but also reduces the bandwidth required for data transfer. To assign tasks dynamically in an edge computing environment, with an intelligent distribution, solutions use techniques such as dynamic Particle Swarm Optimization or directed acyclic graph algorithms which can minimize bottlenecks and offer a scalable solution. Deep learning and neuromorphic hardware offer energy-efficient solutions for the local processing of complex tasks, beneficial for resource-constrained devices. Orchestration tools must be able to have dynamic behavior, being able to adapt to network conditions, energy availability, and device load, which will be essential for a highly dynamic environment such as healthcare. Energy-aware algorithms will be an important part of future edge computing systems, which will involve offloading resource-intensive tasks to more capable nodes or being able to fit them within the available resources at that time. Collaborative frameworks, designed with heterogeneity in mind, enabling seamless cooperation and contribution between devices will be beneficial in healthcare environments where real-time data processing is critical, and the network is composed of a mix of devices. Table 5 presents a SWOT analysis that summarizes the strengths, weaknesses, opportunities, and threats for edge computing in healthcare. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strengths** & **Weaknesses** & **Opportunities** & **Threats** \\ \hline Real-time data monitoring, processing and analysis & Limited computational resources & Growing demand for remote monitoring and telehealth & Security and vulnerabilities of healthcare edge devices \\ \hline Enhanced patient data security and privacy & Complex data orchestration and healthcare management processes & Personalized care with edge AI advancements & Patient safety, data privacy, and integrity \\ \hline Reduced network overhead & Low scalability & Growth in IoT devices adoption for telecare & Network infrastructure limitations in data transmission, processing, and intermittent connectivity \\ \hline Improved reliability for eHealth services & Interoperability and data integration challenges & Development of efficient distributed and federated AI models including LLMs & Fragmented healthcare systems and vendor lock-in \\ \hline AI enabled support for healthcare professionals & High costs for infrastructure setup & Advancements in data encryption & Regulatory constraints \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Edge computing in healthcare SWOT analysis.