ABSTRACT
A growing body of research shows that improving diagnostic and treatment efficiency can save lives. Artificial intelligence (AI) in healthcare is a new research topic. Human engineering and domain expertise were initially necessary to transform raw data into algorithms. One type of machine learning called deep learning creates representations from raw data, with an algorithm determining how much change should be done. That deep learning can learn from huge amounts of data is its utility. It can categorize, analyze, and forecast data to identify patterns. Weak clinical integration makes measuring current effect difficult, but simulation data reveals AI's potential to enhance screening accuracy and efficiency, minimize effort, and potentially diagnose sickness better than experts. In medical imaging, deep learning algorithms categorize, segment, and identify objects in pictures and movies. Studies on AI-based breast cancer, cardiac imaging, and melanoma screening showed promising results. Evolved deep learning algorithms such as convolutional neural networks (CNNs) effectively assess spatially invariant input. In trials assessing their diagnostic utility in object classification, CNNs were close to or at the physician level in identifying skin cancer, cardiovascular risk, and breast cancer. During the COVID-19 pandemic, AI was used for everything from vaccine/drug discovery to diagnosis, according to Abd-Alrazaq. Now, most AI systems actively combine physicians and algorithms, enhancing accuracy and efficiency. © 2023 Elsevier Inc. All rights reserved.