Because a protein’s shape determines its function and the extent of its dysfunction in disease, experts say efforts to illuminate protein structures are central to therapeutic science and the development of new medicines.

If AI algorithms can successfully slash drug development times and increase success ratios, a growing number of patients should have lower drug prices to look forward to.

According to researchers, deep learning methods represent a promising approach for analytics in science for discovering subtle patterns in very complex scientific data of all kinds.

AI has tremendous potential to help practitioners make difficult decisions in an increasingly sophisticated, complicated and powerful world of healthcare, said Gov. Baker.

For one study, researchers are recruiting 100 patients with bipolar disorder or schizophrenia to follow through monthly recorded sessions and data collected from wearable sensors and smartphones.

Artificial Intelligence is definitely something to watch in 2019, says one stakeholder, but distinguishing between the usability of different applications is important.

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.

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.