Predictive medicine uses predictive analytics to determine the risk probability of diseases by analyzing large amounts of data.

Precision medicine tailers treatments and suggests certain drugs for patients based on their medical history.

Physical medicine is an umbrella term for numerous therapies that encourage proper movement and function of the body.

Machine learning is making leaps and bounds in the field of medicine. Its helping doctors, clinicians, and researchers all across the world increase their productivity and better serve the needs of millions of patients every year. But as any emerging technology does, machine learning has its challenges and limitations to face.

Despite the mostly postivite impact macine learning, iOT, and Big Data is having on medicine, there are a few ethical issues we must consider as we continue to build smarter algorithms.

Electronic health records are used by most large hospital systems in the US but not in all countries. As part of the HITECH act, there are now financial incentives for using EHRs and many clinics are adopting them.

A lot of the patient data collected comes from electronic health records. Other patient data can be collected from narrative notes - the text clinicians write about a patient during a visit. But since they all structure data in different ways, holistically mining data from health trackers is difficult.

Yes. Hospitals that use predictive analytics generally do stratify populations into groups based on age, quality of life, and many other factors. Developing predictive algorithms quickly gets difficult because of the number of factors that play into many clinical decisions.