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It is no secret that artificial intelligence (AI) plays an increasing role in medical and health care. There are several applications of AI in medicine and health care: image analysis, genomics, medical record analytics, drug and vaccine development, etc. The future of medicine is personalized and participatory, especially in diagnosis and treatment. Accumulating humanity-scale data on different aspects of human health is key to achieving this goal. The combination of exponentially growing digitally available health data and fast-paced development of computing intelligence and machine learning techniques offers us great promise to deliver smart technology-based infrastructure, tools, and solutions for medicine and health care. Federated learning has emerged as one of the most important distributed privacy-preserving learning paradigms and is proven to be capable of addressing some of the key challenges in medical AI and health data sciences: lack of data, privacy, and biased AI models. In this final chapter, we first introduce the federated learning framework. Then, we go over some key developments of federated learning methods and tools. We also present its most current applications in medical AI, health surveillance, and health data analytics. Finally, we conclude with challenges and potential future directions of federated learning in AI and data sciences for medicine and health care. In traditional machine learning, we assume that the training data is collected and stored in one place. All of the data is available to us to train a learning model. In many important AI applications, the training data is distributed across different locations: e.g., hospitals, clinics, patient residences… but it is not physically shared and brought to one place. In a lot of these applications, the data is sensitive, personal, protected health information. Transferring these data to a different location for training can incur serious privacy and ethical violations. That sensitive data could include the vulnerabilities of patients, such as potential genetic or health risk factors, serious diseases or medical conditions, or even the sexual orientation of those patients.

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Federated Learning and Healthcare 5.0: Paving the Road Ahead for Privacy-Preserving Smart Health Systems

  • 章节

  • 发表日期：

    2026年

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  • Wasswa Shafik

摘要

It is no secret that artificial intelligence (AI) plays an increasing role in medical and health care. There are several applications of AI in medicine and health care: image analysis, genomics, medical record analytics, drug and vaccine development, etc. The future of medicine is personalized and participatory, especially in diagnosis and treatment. Accumulating humanity-scale data on different aspects of human health is key to achieving this goal. The combination of exponentially growing digitally available health data and fast-paced development of computing intelligence and machine learning techniques offers us great promise to deliver smart technology-based infrastructure, tools, and solutions for medicine and health care. Federated learning has emerged as one of the most important distributed privacy-preserving learning paradigms and is proven to be capable of addressing some of the key challenges in medical AI and health data sciences: lack of data, privacy, and biased AI models. In this final chapter, we first introduce the federated learning framework. Then, we go over some key developments of federated learning methods and tools. We also present its most current applications in medical AI, health surveillance, and health data analytics. Finally, we conclude with challenges and potential future directions of federated learning in AI and data sciences for medicine and health care. In traditional machine learning, we assume that the training data is collected and stored in one place. All of the data is available to us to train a learning model. In many important AI applications, the training data is distributed across different locations: e.g., hospitals, clinics, patient residences… but it is not physically shared and brought to one place. In a lot of these applications, the data is sensitive, personal, protected health information. Transferring these data to a different location for training can incur serious privacy and ethical violations. That sensitive data could include the vulnerabilities of patients, such as potential genetic or health risk factors, serious diseases or medical conditions, or even the sexual orientation of those patients.