AI has the potential to accelerate clinical trials

The European Medicines Agency said there is evidence that “both doses of a two-dose Covid-19 vaccine… are needed to provide adequate protection against the Delta variant” – Copyright AFP/File KAREN BLEIER

The healthcare industry is drowning in the sea of ​​unused data they collect. Yet healthcare leaders can potentially benefit from this large amount of data. For example with clinical studies. When conducting clinical trials, patient data (quality control, quantity and availability) is critical to maintaining quality assurance and accelerating the turnaround time required to develop drugs and treatments.

Tim Riely, VP Clinical Data Analytics at IQVIA, has been thinking about how business leaders in the life sciences industry can leverage AI-enhanced data to accelerate interoperability, flexibility/speed, and insightful data visualization.

Tim Riely begins his assessment by considering the scale of data that needs to be processed within healthcare: “Loaded with analyzing more than 1 trillion gigabytes of data annually, life sciences business leaders are reaping significant benefits from AI-enhanced data to transform their data. operations and achieving accelerated results.”

To assist scientists in this task, artificial intelligence, including machine learning algorithms, can be helpful. Here, Riely states, “AI and ML streamline clinical trials and deliver validated real-time data to decision-making teams faster and with more accuracy.”

More specifically, Riely believes such technologies can do the following: “This accelerates the drug development process and minimizes the risks of data drift, increasing workforce productivity and improving data collection.

With concrete cases in the life sciences sector, Riely states: “Biopharmaceutical organizations, for example, are embedding AI throughout the lifecycle of their assets, leading to higher success rates, faster regulatory approvals, shorter time to reimbursement and improved cash flow from the sector. clinical trial process, from start to launch.”

Furthermore, he adds: “AI also helps clinical staff submit documents more quickly to the Trial Master File (a set of documents that prove that the clinical trial was conducted according to regulatory requirements), improving the quality of the data submitted as part collected from the trial is being improved. identify subpopulations of individuals who would benefit most from a treatment and predict risks for a clinical trial.”

As AI itself advances and expands its “thinking capabilities,” healthcare could deliver even more benefits. Riely considers some of these as follows: “As we enter a world of generative AI, we see a positive impact across the industry. In concrete terms, by gaining insights faster through chat interfaces, developing solutions faster with new technical tools, improving discrepancies detection and accelerating document writing, making tasks such as creating protocols and safety stories more efficient.”

There are risks to consider when using such technologies, and Riely is aware of them: “However, as with all new technology implementations, it is also important to take precautions when implementing generative AI. To reach its full potential, the technology must be trained with high-quality, regulatory-compliant data that can provide recommendations to experts who make final decisions. It must also be designed for security, safety and accuracy.”