Artificial Intelligence (AI) has transformed healthcare by providing novel
opportunities to improve patient service, diagnosis, treatment, and administration. This
chapter examines the various AI aspects employed in healthcare, ranging from
enhancing diagnostic precision through medical imaging and machine learning
algorithms to optimizing treatment regimens via personalized medicine and predictive
analytics. The fact that artificial intelligence has successfully permeated every other
branch of science and technology, as well as numerous other distinct fields, is not new.
As the name implies, artificial intelligence refers to the intelligence ypically and
predictably associated with living things, especially humans, but is recreated in a
different form and assigned to computers and/or robots. The fact that AI's applications
can be found in the same foundations from which it eventually evolved—the
“technology” realm itself—makes its pervasiveness in the postmodern world
understandable. According to this claim, every IT industry or online portal uses modern
automated customer service chat. AI is currently revolutionizing the global health
system, saving lives and enhancing their quality. By leveraging the power of AI in data
analysis, medical research can gain unprecedented speed, depth, and accuracy. This not
only accelerates the pace at which discoveries are made but also enhances the quality
and reliability of the findings, promising a brighter future for healthcare advancements.
Recent advancements have demonstrated that AI-powered diagnostic systems achieve
accuracy levels of up to 94.5% in image-based diagnosis (e.g., radiology and
dermatology), rivalling or surpassing those of experienced clinicians. In predictive
analytics, AI models have achieved 85-92% accuracy in the early detection of diseases
such as sepsis and diabetic retinopathy. Notably, the application of AI in Electronic
Health Records (EHR) management has led to a 30% reduction in documentation
time, significantly improving clinician productivity. AI-based robotic surgery
platforms report 21% shorter hospital stays and 34% lower complication rates
compared to traditional approaches. Moreover, machine learning algorithms used in drug discovery have reduced lead compound identification time by up to 60%,
accelerating innovation in pharmacology.
The integration of federated learning ensures greater than 90% model performance
accuracy while preserving data privacy, a critical concern in healthcare.
Keywords: Diagnostics, Drug discovery, Healthcare automation, Medical imaging, Personalized medicine.
