A technologically evolved environment confronts the healthcare business. However, machine learning (ML) technology could revolutionize patient care, diagnosis, and decision-making. This chapter examines machine learning advances that can be applied to e-health. It also discusses machine learning algorithms in predictive analytics, personalized medicine, and early disease diagnosis. The chapter also emphasizes how ML, medical robotics, and AI-assisted diagnostics may improve healthcare delivery. Machine learning advancements in electronic health (e-health) are studied through case studies and real-world applications to determine their practical applications. This chapter also tackles ethical issues and challenges in healthcare ML integration. Readers will understand how machine learning is changing e-health to provide more effective, individualized, and precise healthcare solutions. This exploration contributes to the conversation on incorporating advanced technology into healthcare, paving the way for digital healthcare's technological future.
Top1. Introduction
Within the dynamic and ever-changing realm of healthcare, the convergence of machine learning (ML) technology with electronic health (E-Health) holds the potential to bring about a transformative shift in the way patient care is delivered, diagnoses are made, and decisions are reached. This chapter initiates a thorough investigation into the developments in machine learning that have the potential to revolutionize the digital healthcare paradigm.
As the healthcare industry grapples with the challenges of an increasingly complex environment, the integration of ML emerges as a pivotal solution, offering the potential to enhance predictive analytics, and personalized medicine, and enable early disease diagnosis (Amisha et al., 2019). By delving into the practical applications of ML in E-Health through case studies and real-world examples, this chapter aims to unravel the transformative impact of these technologies. Furthermore, it addresses ethical considerations in the integration of ML into healthcare practices, emphasizing the need for responsible implementation. This Chapter explores the impact of the integration of ML, medical robotics, and AI-assisted diagnostics on the healthcare industry. It highlights how this convergence is transforming healthcare delivery by enabling more efficient, personalized, and accurate healthcare solutions in the era of digital advancements.
1.1 Overview of the Technologically Evolved Healthcare Environment
The healthcare sector has experienced a significant shift in recent times, mostly influenced by the swift progress in technology (Bhambri and Gupta, 2018). The incorporation of advanced technologies has led to the emergence of a healthcare setting that is technologically advanced, signifying a shift away from conventional methods and introducing a novel era of digital healthcare (Chem et al., 2017). This transformation is characterized by the infusion of innovative solutions that leverage the power of technology to enhance patient care, streamline diagnostic processes, and optimize decision-making within the healthcare ecosystem (Khang & Medicine, 2023).
The technologically evolved healthcare environment is marked by the increasing digitization of medical records, the emergence of telemedicine and remote monitoring, and the integration of AI-driven solutions (Esteva et al., 2017). As the healthcare sector embraces these technological innovations, there is a growing recognition of the potential for ML to revolutionize patient care, diagnostic procedures, and overall healthcare delivery.