Artificial Intelligence has changed how doctors diagnose, prevent, and treat diseases. Radiologists used to rely on a trained eye to diagnose, but often misdiagnosed patients as well. Recently AI has introduced computer algorithms that diagnose patients more accurately. My research focused on CheXNet, an algorithm developed by Stanford researchers to evaluate chest X-rays for signs of disease. CheXNet is comprised of a 121-layer convolutional neural network that takes a chest X-Ray image as an input, and outputs the probability of a pathology. The algorithm can diagnose up to 14 types of diseases. Detecting pneumonia in patients using X-ray images is very difficult. Two statistics that stress the significance of getting an accurate answer for patients are that each year one million adults are hospitalized, and 50,000 die in the United States from pneumonia. The objective of my research into CheXNet is to inform students of the evolving role AI is playing in diagnosing disease. Students will learn: How AI can assist humans and how humans can assist AI. Facilitation of accelerated diagnosis & medication nonadherence. Career information for students seeking entrance into the healthcare industry. Researchers collected annotations from four practicing radiologists on a subset of 420 images from ChestX-ray14. Researchers made modifications to CheXNet to detect all 14 types of medical conditions from ChestX-ray14. The radiologists did not have prior access to patient information The researchers found that CheXNet exceeded radiologist performance on pneumonia diagnosis. The results were based on the F1 Score that measures accuracy by incorporating both precision and recall. At a score of 0.435 compared to 0.387 for the radiologists, the scores were significant. The researchers also found CheXNet achieves better results on different pathologies. The algorithm outperformed published results on all 14 pathologies in the ChestX–ray14 dataset. With technology like CheXNet, many hope that artificial intelligence can be integrated to areas where access to skilled radiologists is limited. In conclusion, the algorithm helps reduce the number of missed cases of pneumonia, and by showing radiologists where to look first, artificial intelligence can provide better medical care to patients. According to an article titled “3 ways artificial intelligence is changing the healthcare industry”, between 1988 and 1994, roughly 38 percent of adults living in the United States were taking at least one prescription drug. Later the number would increase to 49 percent with 3.2 billion of those not being taken as directed or not taken at all. The algorithmic tools of AI will better identify which patients experience medication nonadherence and as a result cut out wasted prescriptions. In the above graph from Accenture, AI could fill the gap in disparity between the supply and demand of clinicians. Since growth in the AI health market is expected to reach $6.6 billion by 2021, a background in computer science could help students break into the medical field since must curriculums have not yet shifted from information to the age of artificial intelligence. Mckinsey estimated in 2017 that 50% of activities carried out by workers have the potential to be automated. Something to keep an eye on for professionals seeking a career in healthcare.