Organizational Commitment and the Academic Staff in HEI's in North West India

Smart healthcare technologies have transformed the healthcare landscape, promising precision, efficiency, and improved patient outcomes. However, these advancements bring significant ethical, privacy, and security challenges. The increased use of AI and data-driven systems in healthcare raises questions about patient autonomy, data ownership, and informed consent. As large volumes of sensitive health data are generated, ensuring robust security measures is critical to protecting patient privacy. Moreover, the push for cost-effectiveness in healthcare often conflicts with ethical obligations, necessitating a balanced approach. Health economics also impacts ethical decision-making, as stakeholders must navigate the trade-offs between cost containment and quality care. This chapter explores these complexities, examining frameworks to address ethical, privacy, and security concerns. Through case studies and regulatory perspectives, it offers insights into how smart healthcare can responsibly advance while safeguarding patient rights and public trust.

Ethical, Privacy, and Security Issues in Smart Healthcare

Gupta

2024

An Exhaustive Inference of Machine Learning Applications in Healthcare

Swain,

Kamila,

Jena

et al. 2024

Machine learning has become an important tool in healthcare research to solve complex classification problems effectively, efficiently, and quickly. Generally, doctors treat patients according to their medical knowledge and personal experience. Since different professionals have different experiences, they may sometimes make a wrong diagnosis and need more time for treatment. Current research mainly focuses on the problem of classifying/predicting medical data based on machine learning. There is a need to create an intelligent structure that can distribute the information stored in the database. Human data analysis capabilities are less compared to data storage. This is more important in the case of medical records because it helps search, diagnose, and treat patients based on individual records. This paper's main goal is to review the pre-researched methodologies of machine learning techniques to analyze healthcare data to diagnose and prevent illnesses. Finally, these research articles are classified based on healthcare data, machine learning techniques, and performance parameters.

Navigating Challenges and Innovations in Global Healthcare

Singh,

Chand,

Singh

2024

Telemedicine and remote care have redefined the contours of healthcare delivery by bridging geographical barriers and enhancing accessibility, particularly in underserved and low-income communities. This chapter delves into the evolution of telemedicine, underscoring its transformative role in reducing disparities in healthcare access. It further explores the cost-effectiveness of telehealth models, presenting data to illustrate affordability and long-term economic viability. The chapter concludes by analyzing the macroeconomic impact of expanding telemedicine services, including workforce shifts, infrastructural investments, and implications for healthcare policy. The potential of telemedicine to recalibrate the healthcare landscape is immense, but it demands thoughtful integration with existing systems, regulatory foresight, and continuous technological innovation to ensure equitable, sustainable care.