Content area
Full text
ABSTRACT:
Artificial Intelligence (AI) has revolutionized healthcare by simulating human intelligence to enhance diagnostics, treatment planning, and operational efficiency. Over the decades, AI has evolved through key milestones, from early expert systems like MYCIN to advanced deep learning applications in radiology, pathology, and genomics. Today, Al-driven tools improve disease detection, personalize treatment, assist in robotic surgeries, and automate administrative processes, ultimately enhancing patient outcomes. Al also accelerates drug discovery, supports real-time ICU monitoring, and enables predictive analytics for proactive healthcare management. However, challenges such as data privacy, algorithmic bias, over-reliance on AI, and regulatory concerns must be addressed to ensure ethical and equitable implementation. While AI cannot replace human judgment and empathy, it serves as a powerful adjunct to clinical decision-making, improving accuracy, efficiency, and accessibility in healthcare. Thoughtful integration of AI can bridge gaps in healthcare delivery, fostering a future of data-driven, patient-centered medical care.
KEYWORDS: Artificial Intelligence, Machine Learning, Clinical Decision Support Systems, Healthcare Automation and Medical Informatics.
INTRODUCTION:
Artificial Intelligence (AI) refers to the simulation of human intelligence by machines, particularly computer systems. These systems are designed to perform tasks such as learning, reasoning, problem-solving, understanding language, and perception-tasks typically requiring human intelligence1.
Artificial Intelligence (AI) is revolutionizing modern healthcare by enhancing clinical decision-making, improving diagnostic accuracy, and streamlining administrative processes. Defined as the simulation of human intelligence by machines, Al enables systems to learn, reason, and solve problems-functions that traditionally required human expertise. Over the decades, AI's entry into healthcare has been marked by key milestones, from early expert systems like MYCIN to advanced deep learning models in diagnostic imaging and genomics. Today, Al-powered tools support a wide range of applications including pathology, radiology, robotic surgery, personalized treatment, drug discovery, and remote monitoring, thereby improving patient outcomes and operational efficiency.
AI also plays a critical role in research, aiding both qualitative and quantitative methodologies and accelerating systematic reviews through automation and machine learning. Despite its transformative potential, Al in healthcare faces significant challenges. These include ethical concerns, data privacy issues, algorithmic bias, lack of human empathy, and high implementation costs. Moreover, questions of accountability and equitable access remain unresolved. Hence, while Al is a powerful ally to clinicians and researchers, it must be thoughtfully integrated to ensure it enhances rather...