Artificial intelligence can predict people's health problems over a decade into the future, say scientists. The technology has learned to spot patterns in people's medical records to calculate their risk of more than 1,000 diseases. The researchers say it is like a weather forecast that anticipates a 70% chance of rain - but for human health. Their vision is to use the AI model to spot high-risk patients to prevent disease and to help hospitals understand demand in their area, years ahead of time. The model - called Delphi-2M - uses similar technology to well-known AI chatbots like ChatGPT.

An unimaginable amount of data is continually being generated by scientific experiments, longitudinal studies, clinical trials, and hospital records--but what can be done with all this information? Barbara Engelhardt (she/her), PhD, is building machine-learning models and statistical tools to make use of that data and find ways to better understand, and even prevent, disease. She is now joining Gladstone Institutes as a senior investigator. "Barbara is an innovator in computational biology," says Katie Pollard, PhD, director of the Gladstone Institute of Data Science and Biotechnology. "She brings vast expertise in statistical models and will help expand our machine-learning program. We're thrilled she's joining our team."

Artificial intelligence has the potential to "prevent disease", an expert has said. Dr Dominic King, UK lead at Google Health, said such technology could allow health problems to be predicted rather than detected. Speaking at the Royal Society and the Academy of Medical Sciences' Healthy Ageing conference, he added that AI was no substitute for exercising and not smoking. Dr King continued: "Artificial intelligence has, I think, the potential to prevent disease. "I do think there's a real role for these approaches to allow us to predict, rather than detect health problems.

Personalized Analytics is becoming essential in healthcare, stemming from the movement from fee-for-service to a value-based market. The need to preempt and prevent disease on a more personal level, rather than merely reacting to symptoms, has created a significant opportunity for machine learning-based applications. This "analytics of one" approach (using advanced mathematical models and artificial intelligence techniques) is already impacting several key areas: Prime examples include cardiac imaging analysis that aides physicians in assessing conditions, including heart attacks and coronary artery disease, and retinal image analysis to detect diabetic retinopathy. The anticipated goal for AI in healthcare is to enhance and expand the "four Ps" of care delivery – predictive, preventative, personalized and participatory. Predictive: Predictions have existed in healthcare for some decades now, as statistical models based on structured data sources.

Big data, robotics and Artificial Intelligence (AI) are radically changing the way clinicians diagnose and treat disease. Gone are the days of "one size fits all" treatment protocols. Instead, healthcare providers are using centralized data sets that are AI analyzed to provide targeted, personalized healthcare that focuses on prevention rather than cure. In addition to a swing toward preemptive healthcare, big data is central to the sector's commitment to capping wasteful expenditure. Billions that were once raised to manage duplicated records at hospitals, clinics and doctors' surgeries are now being used for more beneficial outcomes.