The healthcare sector has benefited greatly from the integration of AI/ML with distributed technologies like edge computing, blockchain, and Internet of Things (IoT) to address challenges like data interoperability, security, and scalability. This synergy has a major impact on patient care, medical research, and the efficiency of the healthcare system. AI/ML techniques are used in a variety of fields, including drug development, medical imaging interpretation, picture identification, predictive analytics, and sickness prediction. The relationship between AI/ML and distributed technologies—such as decentralized architectures for safe access to real-time data sources, blockchain for data integrity and privacy, and edge computing for low-latency processing—is discussed. When combining AI/ML with dispersed technology, the healthcare business faces trends and concerns related to interoperability, legal compliance, and ethical issues.
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Healthcare is undergoing a change thanks to the combination of distributed technology and AI/ML methods, which improve patient care and spur medical innovation. This chapter examines how these technologies might work together to strengthen healthcare systems and spur innovation in medicine. Healthcare issues such data interoperability, security, and scalability can be effectively addressed by distributed technologies like blockchain, edge computing, and the Internet of Things. These technologies are decentralized. Blockchain promotes trust in transactions and medical records by guaranteeing data integrity(Egala et al., 2023a). Real-time data processing is made possible by edge computing, which speeds up decision-making. IoT devices gather data generated by patients, offering insights into behavioral patterns and health parameters.
Deep learning, natural language processing, and reinforcement learning are a few AI/ML techniques that provide sophisticated data analytics, predictive modeling, and decision-making skills in the healthcare industry. Their proficiency is in activities such as disease detection, treatment optimization, and patient monitoring. Through the analysis of vast datasets, they empower doctors to make informed decisions and customize patient care. Data security, interoperability, and real-time insights are improved in healthcare applications through the combination of distributed technology and AI/ML approaches. While edge computing reduces latency and allows for real-time patient monitoring, blockchain improves data security. IoT devices provide AI/ML models with data, enhancing predictive analytics and facilitating preemptive measures to avert unfavorable health outcomes(Ullah et al., 2023).
The advancement of precision medicine and tailored healthcare could be facilitated by the confluence of distributed technology and AI/ML approaches. Healthcare professionals can enhance treatment outcomes, minimize adverse reactions, and optimize resource allocation within healthcare systems by customizing treatments based on each patient's genetic composition, lifestyle, and medical history(Ohanenye et al., 2021).
Healthcare organizations confront a number of obstacles when integrating distributed technology with AI/ML techniques, including privacy issues, ethical concerns, and regulatory compliance. Data silos and interoperability problems between different systems make it more difficult for stakeholders in the healthcare industry to collaborate and exchange data smoothly. This chapter explores the possible advantages and difficulties of using AI/ML in distributed technologies for healthcare applications. In an effort to offer insights on how healthcare organizations might use these technologies to improve patient outcomes and spur innovation in the quickly evolving healthcare sector, it looks at case studies, best practices, and new trends(Ghosh et al., 2023).
The healthcare sector has undergone a change thanks to the incorporation of AI and distributed technology. In addition to AI and machine learning, the application of blockchain, edge computing, and IoT can enhance patient outcomes and increase productivity. This chapter highlights the possibilities of this creative approach in the healthcare industry by examining the dynamic environment of bolstering distributed technologies with AI/ML approaches for healthcare applications. The integration of distributed technologies and AI/ML approaches in healthcare is examined in this chapter, with an emphasis on the principles, methodology, applications, and synergies between them(Semantha et al., 2020). By improving data security, interoperability, predictive analytics, and real-time decision-making, the integration presents a wealth of chances for innovation and change.
The importance of distribution technologies in healthcare applications—such as wearables with Internet of Things capabilities for remote patient monitoring, decentralized electronic health records, and edge computing for telemedicine—is examined in this chapter. It also looks at how to combine AI and ML with dispersed technologies, analyzing the effects on patient care and healthcare delivery. The study investigates the application of distributed technologies in healthcare, such as decentralized clinical trials and supply chain management based on blockchain. It addresses both possibilities and problems by offering insights into the strategic implications, ethical issues, and best practices for utilizing these technologies to spur innovation and enhance healthcare outcomes(Egala et al., 2023b).