Data storage and software applications are using machine learning to derive a patient’s diagnosis. The intensity of individual pixels in images over a period of time can be analyzed to help detect lung cancer patients. 

Machine learning applications are used to derive a diagnosis by storing and analyzing years of demographic information for thousands of patients. When a new patient arrives who is similar to a filtered base in the large data store, the archived information is used to help suggest possible patient diagnoses.

“Machine learning plays, I think, an essential role in medical image analysis nowadays.”
--Kenji Suzuki, Assistant Professor of Radiology and Medical Physicians at the University of Chicago’s Comprehensive Cancer Center

Machine learning helps predict health issues using volumes of data including age, blood pressure, and lab results. Hardware storage and speed improvements over the past five years have allowed more information to be stored and accessed by a physician during exams. 

“As the use of electronic medical records gains acceptance, machine learning is likely to play an even larger role in clinical medicine.”
--Neil Savage, Communications of the ACM

Software involving large amounts of data is continuously being developed, giving the doctor more tools to determine a diagnosis while the patient is present.

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