With artificial intelligence (AI), our healthcare industry is poised to make an exceptional leap forward, helping us care for our patients and better understand the practice of medicine. This chapter investigates AI-enabled biomarker discovery and disease detection, their revolutionary impact on biomarker discovery and disease detection against the difficulties of conventional methods, and how AI overcomes these difficulties. Understanding disease mechanisms, predicting disease development, and identifying targeted therapeutics depend on biomarkers and measurable signs of biological processes. However, commercial biomarker discovery often overlooks the nuance of disease progression and therapeutic response. Typical disease diagnosis may be subjective, time-consuming, and susceptible to human error. However, AI offers a new paradigm, allowing one to examine huge, intricate data much faster and more precisely than existing approaches. With AI, complex patterns can be discerned, and forecasted results can be obtained, enabling the discovery of smarter biomarkers and making more intelligent disease detection. In this chapter, artificial intelligence in healthcare is reviewed along the lines of machine learning, deep learning, and natural language processing, as well as a discussion of its past application in biomedical sciences. Then, it explores the specific applications of AI in biomarker discovery, focusing on high-throughput data integration, feature selection, and biomarker detection. In this chapter, AI is studied in its roles in disease diagnosis, such as clinical decision support systems, AI-enhanced imaging techniques, predictive models, and personalized medicine. Ethical implications and issues concerning AI in healthcare are explored, along with innovative AI methodologies of multimodal approaches and explainable AI. The future directions and trends of AI in medical research and patient care are outlined before concluding this chapter.