In the last few decades, artificial intelligence (AI) has shown rapid growth in
medicine with the evolution of computer vision, robotics, natural language processing,
and deep neural networks. The technology has also been applied to healthcare with an
inexact thought that AI will replace the workforce. AI works as a helping hand for
human clinicians because a machine can never replace a human brain. Present
healthcare systems can implement AI technology for diagnosing patients and their
treatments, drug invention, prediction of disease outbreaks, real-time monitoring of
critical patients, radiology, and many more. The latest achievements by Google for the
diagnosis of cancer, and diabetic retinopathy by JAMA using deep learning algorithms
and surgical robots show substantial shifts in medicine. A simple assessment of
electronic health records (EHR) provides more opportunities for the medical experts
during the invention and application of improving medicines. The coming future of
health care depends on the advancement of AI. However, with ease comes difficulty,
such as the privacy of data and causality problems which should be considered when
deploying such strategies.
Keywords: Artificial Intelligence (AI), Deep Learning (DL), Medicine epidemiology, Natural Language Processing (NLP), Neural Network, Support Vector Machine (SVM).
