Precision medicine is one of the most exciting and encouraging advances in healthcare today. It is moving us from one-size-fits-all healthcare to personalized, data-driven treatment that enables more efficient spending and improved patient outcomes. As defined by the National Institute of Health (NIH), Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Doctors and researchers can use precision medicine to more accurately predict which treatment and prevention strategies will work best for a particular patient.
In other words, precision medicine offers a path to helping people recover from illness faster – and stay healthy longer. However, there are barriers to the widespread adoption of precision medicine. These include an ever-increasing amount of data, a shortage of specialists, and the exorbitantly high costs of drug development.
As Artificial Intelligence (AI) enters the precision medicine picture, it can help organizations capitalize on precision medicine in multiple ways. In terms of data challenges, AI leverages deep learning approaches to overcome the obstacles inherent in large data sets and unstructured data. In clinical settings, AI functions as an assistant that helps clinicians work more efficiently and make more accurate diagnoses, which helps improve the productivity of healthcare workers. And at a broader level, AI helps companies accelerate drug development to cut costs and achieve faster time to medicine while reducing errors in the system.
To make this story more tangible, let’s consider a few examples of use cases for AI in precision medicine.
Problem: China has too few expert radiologists to serve a population of 1.3 billion people. To exacerbate the problem, the nation’s 80,000 radiologists spend most of their time looking at normal images, leading to delayed diagnosis for the abnormal cases.
Solution: An AI-based solution running on Intel® Xeon® Processors has been commercially deployed in multiple hospitals, including one of the top ten facilities in China, and has already detected abnormal thyroid nodules in over 5,000 patients. It is estimated that at full deployment this solution could shift 70% of the initial screening workload to less experienced clinicians. This allowed the experienced clinicians to focus on the more challenging cases, increasing their job satisfaction.
