While the majority of headlines concerning AI in healthcare, recently, have likely been focused on the myriad ways AI has been tapped to help combat the coronavirus, use cases certainly continue to multiply in other areas, as well.

For example, in a recent interview at Outsourcing-Pharma, data management executive Jennifer Bradford explained how AI is rapidly changing the face of clinical research.

“The most successful and documented uses of AI in clinical trials have been for target identification and drug repurposing, given the large volumes of structured data available in this area,” she began. “ML-based predictive analytics can also be used in recruitment and retention activities, for example, identifying the right candidates at a faster rate, which can accelerate R&D timelines.”

From a clinical operations perspective, Bradford continued, AI applications are supporting data management teams by automatically detecting faulty data. “For example, in risk-based management, going beyond a rule-based approach applied to the clinical and meta data to identify problematic sites or even patients based on patterns of behavior, identification of outliers etc. As clinical studies continue to become more complex, for example by more complex study designs or through the use of remote monitoring technology, it is important that the data generated is used in the optimal way during the trial. Powerful ML technologies have the potential to monitor this data as it is generated; identifying issues and inconsistencies as trials are ongoing.”

Bradford also pointed to the role AI could play in remote monitoring, enabling researchers to monitor multiple measurements from patients as they go about their daily lives. 

“This in turn will generate large volumes of data,” she added, “data that would be near impossible for a clinician to monitor and analyze across a number of patients on a regular basis.

ML technologies could be used here to flag to a medical team certain changes, potential issues or anomalies for a particular patient, directing the medical team to take further action if they deem it necessary following review of the data.”

Of course, AI is not really a plug-and-play proposition.  According to Bradford, both AI and machine learning come with “a big learning curve. For all consumers of AI technology, whether that be a sponsor, medical teams at a site, or patients, it is important that they understand the limitations of the technology and the context in which it can be used; that AI is only ever as good as the data from which it was built and it may not always have all or even the right answers.

For decision makers, those that may use the output of AI technologies, they of course have the challenge of acting on results. AI after all is a machine, not a human; It has no emotions or empathy and won’t consider the ethical implications of a decision.”

Overall, she said, “these technologies will be embraced and while we need to ensure that the applications are carefully considered and regulated, there is potential for AI and ML to bring us benefits in ways we cannot yet imagine.”