Medicine Meets Big Data: Clinicians Look to AI for Disease Prediction and Prevention

From music streaming platforms to social media feeds and search engines, algorithms are used behind the scenes to tailor services to the unique preferences of individuals. Though the use of algorithms has been explored in health care since the origins of artificial intelligence, new strides in deep learning methods over the last decade are allowing clinicians to go after mass amounts of data that were previously inaccessible, transforming how doctors and clinical researchers detect, diagnose and treat disease. In addition to higher data-computing capacities and advanced algorithms, clinicians can now input data through written and spoken words rather than only quantitative lab and imaging results. As they talk with patients about subjective feelings and pain levels, detailed interpretations can be coded to augment "poking and prodding" data collected through sensors, giving machine-learning algorithms a fuller picture. With enough input, algorithms will be able to output a series of patterns which physicians can then use in their clinical practice for better diagnoses and understandings of disease.