Application of Artificial Intelligence in Healthcare: A Comprehensive Study

Artificial Intelligence (AI) is changing the way healthcare systems work all over the world. This paper presents a comprehensive study of how AI is applied in different areas of healthcare, such as disease diagnosis, medical imaging, drug discovery, patient monitoring, and hospital management. The study explains the basic ideas of AI, machine learning, and deep learning in simple language so that readers with a general engineering background can follow them easily. A detailed literature review is carried out to understand the current trends, techniques, and tools used by researchers and healthcare providers. The paper also discusses common challenges faced while using AI in healthcare, such as data privacy, lack of good quality data, and trust issues among doctors and patients. A review-based methodology is followed, which includes searching relevant research papers, screening them using set criteria, and analyzing them under different healthcare application areas. The findings show that AI, especially machine learning and deep learning models, can help doctors detect diseases early, reduce diagnostic errors, and save time in routine hospital tasks. However, the study also finds that AI systems still need large amounts of good quality data and proper testing before they can be fully trusted in real hospitals. The advantages, limitations, and future scope of AI in healthcare are discussed in detail, along with sample illustrative comparisons drawn from the reviewed literature[1-4]. This study will be useful for students, researchers, and healthcare professionals who want to understand the present state and future direction of AI applications in the healthcare sector.