Fast and accurate medication identification - Delgado, Natalia Larios; Usuyama, Naoto; Hall, Amanda K.; Hazen, Rebecca J.; Ma, Max, Siva Sahu, Jessica Lundin (Nature Digital Medicine, 2019)
Much of the AI work in healthcare is focused around disease prediction in clinical settings, which is an important application that has yet to deliver in earnest. However, there are other fundamental aspects like helping patients and care teams interact and communicate in efficient and meaningful ways, which could deliver quadruple-aim improvements. After heart disease and cancer, preventable medical errors are the third leading cause of death in the United States. The largest subset of medical errors is medication error. Providing the right treatment plan for patients includes knowledge about their current medications and drug allergies, an often challenging task. The widespread growth of prescribing and consuming medications has increased the need for applications that support medication reconciliation. We show a deep-learning application that can help reduce avoidable errors with their attendant risk, i.e., correctly identifying prescription medication, which is currently a tedious and error-prone task. We demonstrate prescription-pill identification from mobile images in the NIH NLM Pill Image Recognition Challenge dataset. Our application recognizes the correct pill within the top-5 results at 94% accuracy, which compares favorably to the original competition winner at 83.3% for top-5 under comparable, though not identical configurations. The Institute of Medicine claims that better use of information technology can be an important step in reducing medication errors. Therefore, we believe that a more immediate impact of AI in healthcare will occur with a seamless integration of AI into clinical workflows, readily addressing the quadruple aim of healthcare.