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The rapid development of artificial intelligence (AI) technologies in recent decades has led to innovation and new development opportunities in many industries. The application of AI technologies in the medical and healthcare sector offers significant potential benefit. In this paper, the integration of AI into healthcare research is introduced to encourage more medical and healthcare experts to research this promising cross-disciplinary area. After introducing the basic concepts that underlie AI, the two major schools of machine learning approaches, namely 'supervised learning' and 'unsupervised learning', are discussed. Next, two commonly used algorithms (artificial neural networks and decision trees) are discussed. The paper then focuses on three healthcare applications of AI technologies, including predicting postoperative mortality, quality of life in older adults, and risk of dementia. Finally, the challenges to integrating AI into healthcare research such as class imbalance, missing data, and data scarcity are discussed along with feasible approaches to resolving these challenges.