Some say that artificial intelligence (AI) will radically change healthcare in the future. But that prediction overlooks an important detail: AI is already significantly changing healthcare. IBM (NYSE:IBM) Watson Health general manager Deborah DiSanzo spoke at the annual J. P. Morgan Healthcare Conference on Wednesday. She provided an update on the progress that IBM Watson, the AI system famous for beating Jeopardy! DiSanzo highlighted four areas where AI is making a big difference today.

From anywhere and with just a mobile phone, anyone can become an air traffic controller, or at least a virtual air traffic controller. One can follow the world traffic flow of airplanes live and find out where an aircraft is coming from and where it is headed. One just has to take advantage of the millions of pieces of data that fly across the Internet. This is the magic power of Big Data. Artificial intelligence then enters the picture to find patterns and give meaning to the massive and heterogeneous information stream.

From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of highquality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a "rapid learning" community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient and use these results to individualize therapies. Research opportunities include adaptively planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge and to continually update that knowledge to benefit subsequent patients.

Currently in the United States, it is estimated to infect 3 to 5 million persons, is the leading cause of death in adults from 14 to 35, and is the nation's leading cause of productive years of life lost aggregated over all age groups. HIV is estimated to infect 40 to 50 million persons worldwide (CDC 1997). The high rate of HIV viral mutation both makes development of a vaccine difficult and results in rapid positive selection for drug-resistant mutant strains. Recent multidrug combination therapies are encouraging but in most cases ultimately fail because of the development of drug resistance (O'Brian et al. 1996). A general theory of HIV drug resistance still is not in hand, but a number of specific sequence mutations in the HIV genome have been described in the scientific literature and associated with increased resistance to certain drugs.

Figure 1 illustrates grasshopper infestation densities in the western United States during 2000, a fairly typical year. In years of heavy infestation, grasshopper densities and economic losses might be much higher. For example, during the 1986 to 1987 outbreak, over 20 million acres of rangeland were treated for grasshoppers in the western United States at a cost of more than $75 million. In Wyoming, the estimated total annual loss to grasshoppers is roughly $19 million. The southeastern quadrant of the state is particularly prone to grasshopper infestations, with significant areas of high-grasshopper densities in 30 of the last 34 years.

AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high. I have therefore taken the liberty of dividing the last 30 years of medical AI research into three eras: the era of diagnosis, the era of managed care, and the era of molecular medicine. A description of these eras allows me to review for you some of the early and current work in AIM and then tell you about some of the exciting opportunities now emerging. Why is AI in medicine even worth considering? In the late 1950s, medicine was already drawing the attention of computer scientists principally because it contains so many stereotypical reasoning tasks. At the same time, these tasks are fairly structured and so are ...

This article explores the premise that a formalized representation of empirical studies can play a central role in computer-based decision support. The specific motivations underlying this research include the following propositions: (1) Reasoning from experimental evidence contained in the clinical literature is central to the decisions physicians make in patient care. Furthermore, the model can help us better understand the general principles of reasoning from experimental evidence both in medicine and other domains. Roundsman is a developmental computer system that draws on structured representations of the clinical literature to critique plans for the management of primary breast cancer. Roundsman is able to produce patient-specific analyses of breast cancer-management options based on the 24 clinical studies currently encoded in its knowledge base.

Researchers have used machine learning to identify how individual stroke patients might respond to different medications, based on the unique structure of their brain. An experiment by University College London (UCL) found that applying computer intelligence to data from from people who had suffered a stroke allowed researchers to see what effect drugs had on brains with varying patterns of damage. For the study, a machine learning algorithm was applied to CT and MRI scans of 1172 stroke patients and mapped the anatomical pattern of damage throughout the brain of each individual. The researchers then simulated the effects of certain hypothetical drugs, to see if any reactions that would have been missed by conventional methods could be identified. They found that the algorithm was particularly advantageous when looking at medication effects that reduced the size of lesions in patients' brains.

She consults an app on her phone, which asks an increasingly sophisticated series of diagnostic questions. The app also takes in data from Janet's fitness trackers that monitor heart rate, blood pressure and blood sugar. The app decides that Janet's symptoms look serious, and it arranges a video chat with a human doctor to discuss options so that potentially bad news can be presented in a more "human" way. The doctor has access to Janet's data remotely, along with access to a more sophisticated diagnostic, Artificial Intelligence. During that consultation, Janet is booked into a clinic for medical imaging scans to aid in further diagnosis.

Amazon meanwhile offers medical advice through applications on its voice-activated artificial assistant Alexa. Insilico is working on drugs for amyotrophic lateral sclerosis (ALS), cancer and age-related diseases, aiming to develop personalised treatments.