Abstract
With the escalating global population, the healthcare sector faces unprecedented challenges, necessitating innovative solutions. Deep learning (DL) and federated learning (FL) have emerged as pivotal technologies, yet challenges persist in data privacy, security, and model interpretability, especially in healthcare applications. This research addresses these challenges by proposing robust frameworks for secure, privacy-preserving federated learning with explainable artificial intelligence in smart healthcare systems. The objective is to enhance the security, performance, and privacy of healthcare systems, ensuring their resilience and effectiveness in real-world scenarios. The research employs a literature approach. This comprehensive approach establishes a foundation for the future development of smart healthcare systems, fostering trust, transparency, and efficiency in healthcare decision-making processes.
TopIntroduction
The escalating global population has profoundly impacted the healthcare sector, necessitating innovative solutions to address the challenges posed by increased demands for medical resources (Bhattacharya., 2023). Stakeholders throughout the healthcare industry may gain a great deal from the discoveries made via studies of safe, privacy-preserving federated learning with explainable artificial intelligence for innovative healthcare systems. Healthcare professionals can use the research findings to enhance their decision-making processes. By leveraging secure federated learning models and interpretable AI, they can make more accurate diagnoses, tailor treatments based on patient's genetic profiles, and interpret medical images with greater precision. This research helps doctors make more precise diagnoses, which lowers the potential for error and makes personalized treatment regimens possible. This leads to better patient outcomes and enhanced trust between healthcare professionals and patients. Patients can benefit from personalized healthcare interventions based on their genetic makeup, lifestyle choices, and medical history. As a result, they are more likely to participate in their care actively and stick to their treatment programs. The research helps people take control of their health by providing them with more information. The ability to have a say in one's healthcare choices is associated with improved mental and physical health. Researchers can use secure federated learning frameworks to collaborate across institutions, leading to more robust and diverse datasets for research. They can also employ interpretable AI techniques to gain insights from complex models, facilitating the discovery of new medical knowledge. The study contributes to accelerating medical research by enabling secure and privacy-preserving collaboration. It enhances transparency and understanding of AI-driven models, facilitating the discovery of novel insights and potential breakthroughs in healthcare. Healthcare institutions can implement secure federated learning systems to optimize resource utilization, enhance data privacy, and improve the overall efficiency of healthcare services.
Administrators can make informed decisions based on interpretable AI outcomes, ensuring better resource allocation and patient care. The study contributes to the improvement of healthcare services' efficiency and effectiveness. It aids administrators in making data-driven decisions, leading to better resource management and ultimately improving the quality of healthcare services. Policymakers and regulators may use the results of this study to establish rules and guidelines for the safe and responsible use of AI in medical settings. Safeguarding patients' rights and ensuring the ethical deployment of AI technology may be achieved by establishing standards for data privacy, security, and model interpretability. This research helps form rules and recommendations for the responsible use of AI in healthcare settings. It ensures that adopting AI technologies aligns with regulatory standards, promoting responsible innovation in the field. The research's contributions lie in empowering healthcare professionals, patients, researchers, institutions, and policymakers with advanced, secure, and interpretable AI technologies. By enhancing decision-making, promoting transparency, and ensuring privacy, the study significantly improves the present situation in the healthcare sector, fostering a more efficient, personalized, and trustworthy healthcare ecosystem.
Key Terms in this Chapter
Interpretability: It is the capacity to explain and comprehend a model's choices, forecasts, or actions. Humans can learn from and use an interpretable model since it sheds light on the model's inner workings. For healthcare organizations and individuals to feel confident in the decisions being made on their behalf, interpretable models are essential.
Federated Learning: Federated Learning is a machine learning technique in which a model is trained using data samples stored locally on a network of dispersed edge devices (such as smartphones, IoT devices, or local servers). Instead of sending raw data to a centralized server, model updates are calculated locally, aggregated, and then sent back to improve the global model. Federated Learning makes it possible to train models collectively while protecting individual users' personal information.
Anomaly detection: Anomaly detection refers to identifying patterns, events, or observations that deviate significantly from the expected or normal behavior within a dataset. In healthcare, anomaly detection involves recognizing irregularities or unusual patterns in patient data, medical images, or physiological signals that might indicate potential diseases, errors, or outliers.
Healthcare Systems: Healthcare systems are the structured groupings of people, facilities, and tools used to provide medical treatment to individuals and communities. Everything from medical facilities to doctors and nurses to office workers to hospital furniture to computer networks is part of these networks. Providing timely, affordable, and high-quality medical treatment to people is the core objective of healthcare systems.
Privacy-Preserving: Methods and protocols that preserve privacy while allowing data analysis and exchange are collectively called privacy-preserving strategies. Regarding patient information, privacy-preserving methods guarantee its safe handling, sharing, and analysis without compromising the patient's privacy. For patient privacy and regulatory compliance, healthcare apps must use privacy-protecting techniques.