AI and medicine

For centuries, physicians and healers focused primarily on treating acute problems such as broken bones, wounds, and infections. "If you had an infectious disease, you went to the doctor, the doctor treated you, and then you went home," says Balaji Krishnapuram, director and distinguished engineer at IBM Watson Health. Today, the majority of healthcare revolves around treating chronic conditions such as heart disease, diabetes, and asthma. Treating chronic ailments often requires multiple visits to healthcare providers, over extended periods of time. In modern societies, "the old ways of delivering care will not work," says Krishnapuram. "We need to enable patients to take care of themselves to a far greater degree than before, and we need to move more treatment from the doctor's office or hospital to an outpatient setting or to the patient's home." Unlike traditional healthcare, which tends to be labor-intensive, emerging models of healthcare are knowledge-driven and data-intensive. Many of the newer healthcare delivery models will depend on a new generation of user-friendly, real-time big data analytics and artificial intelligence/machine learning (AI/ML) tools. Identifying risks, determining who is at risk, and identifying interventions that will reduce risk. Supporting and enabling customized self-care treatment plans for individual patients, monitoring patient health in real time, adjusting doses of medication, and providing incentives for behavioral changes leading to improved health. Optimizing healthcare processes (everything from medical treatment itself to the various ways insurers reimburse providers) through rigorous data analysis to improve outcomes and quality of care while reducing costs.