According to researchers, the study illustrates the value of machine-learning for risk prediction within a traditional epidemiological study design, and how this approach might be reported to assist scientific verification.

Some US health firms are already deploying AI on the front lines of the opioid epidemic, using a predictive model to comb through customers' behavioral health claims and other data to flag consumers who might be susceptible to drug overdoses.

The excitement around any new technology comes with a side order of fears about how that system or service will affect people, and, even in healthcare, AI is no exception.

With AI, researchers are able to analyze online web forums and social media platforms to gather real-life patient experiences and responses to new cancer drugs and treatments.

Recent advances in deep learning and neural networks have shown some success in predictions for health outcomes, but CMS says many barriers remain to implement these tools at scale.

Anthem Blue Cross and Stanford University hope to lead the way in using artificial intelligence to tackle price transparency and other healthcare concerns.

When it comes to evaluating AI, says one expert, we should recognize that there are still many questions to ask about how AI can best operate so that it properly augments medical professionals without adversely impacting overall public health.

Specific strengths of AI, says one platform developer, can be a very good match in filling in some of human nature's organic gaps and, by extension, helping doctors provide safer, more thorough treatment to their patients.