International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 02 | Feb 2024
p-ISSN: 2395-0072
www.irjet.net
Revolutionizing Patient Care using AI: A Review of IoT, Machine Learning, and Generative AI in Healthcare Anushree Jain1, Shanu Kuttan Rakesh2 1,2Department of Computer Science and Engineering,
Chouksey Engineering College, Lal Khadan, Masturi Road, NH-49, Bilaspur, Chhattisgarh, India. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Healthcare has undergone revolutionary
artificial intelligence operates. In essence, AI refers to computer models and programs designed to replicate human-level intelligence, engaging in cognitive functions such as intricate problem-solving and gathering experiences.
transformation as a result of the development of artificial intelligence (AI) and the internet of things (IoT). This narrative study examines the applications and consequences of generative adversarial networks (GANs) and machine learning (ML) in healthcare. Statistical models exhibit encouraging outcomes in machine learning applications, which span from predictive diagnosis to optimizing hospital workflows. Meanwhile, through data collection and processing, the Healthcare Internet of Things (H-IoT) has become a key player, transforming patient care. Additionally, this research explores the use of transformers and diffusion models—two forms of generative AI—in the medical field. These models have had a major impact on data reconstruction, drug synthesis, and diagnostic accuracy, ranging from improving medical imaging to protein structure prediction and medication design. The examination addresses related issues like trust, truthfulness, and privacy in addition to gathering the most recent applications. Future directions are examined, emphasizing the possibility of conversational interfaces powered by AI and the developing application of generative AI in healthcare. The expanding significance of AI, IoT, and generative technologies in healthcare is highlighted in the paper's conclusion. These technologies have the potential to significantly influence how healthcare is delivered in the future as long as they develop and meet the particular needs of the medical field.
The bulk of AI tools on the market are classified as "Narrow AI," which denotes that the technology is superior to humans in certain, well-defined task areas. Machine learning techniques, which enable computers to learn, carry out tasks, and modify their performance without requiring direct human intervention, are used in many of these artificial intelligence applications. In 1955, the phrase "artificial intelligence" was first used in a proposal for a conference at Dartmouth College. However, it wasn't until the early 1970s that artificial intelligence (AI) applications made their way into the healthcare industry with the creation of MYCIN, a program meant to help with blood infection treatment identification. The momentum of AI research continued, leading to the formation of the American Association for Artificial Intelligence in 1979, which is now known as the Association for the Advancement of Artificial Intelligence (AAAI). During the 1980s and 1990s, the evolution of AI systems contributed to significant medical advancements. This included improvements such as accelerated data collection and processing, assistance in more precise surgical procedures, extensive research and mapping in the field of database administration (DBA), and the widespread implementation of electronic health records for more comprehensive healthcare management.
Key Words: Patient Care, Artificial Intelligence (AI), Healthcare, Machine Learning (ML), Generative Adversarial Networks (GANs), Internet of Things (IoT).
2. HEALTHCARE EVOLUTION FROM ML
1. INTRODUCTION
Machine learning represents a distinct subset of artificial intelligence, enabling systems to learn and discern patterns from data with minimal human intervention. Rather than being explicitly instructed on what actions to take, computers employing machine learning are presented with patterns and data. Subsequently, they autonomously draw conclusions and enhance their performance based on the information provided. This approach contrasts with traditional programming where computers are explicitly programmed with specific instructions.
The integration of artificial intelligence (AI) in healthcare is far from a recent development. Dating back to the 1970s, the initial applications of AI were employed to address biomedical challenges. Since then, AI-powered applications have undergone significant expansion and adaptation, revolutionizing the healthcare sector. This transformation has led to cost reduction, enhanced patient outcomes, and overall improvements in operational efficiency. Prior to delving into the progression of AI in healthcare, it's helpful to grasp the fundamentals of how
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