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The FDA is seeking to speed up innovations in clinical trials, medical product development, and artificial intelligence using cost-effective strategies and big data.

Source: Thinkstock

By Jessica Kent

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- The FDA is turning to cost-effective strategies and big data to accelerate clinical trial efficiency, medical product development, and innovations in artificial intelligence, according to a recent blog post from FDA Commissioner Scott Gottlieb, MD.

“Our longstanding goal for medical care is to ensure that the right drug or device is delivered to the right patient at the right time. This vision is increasingly possible with the innovative products that are becoming available,” he wrote.

“These new technologies offer transformative opportunities. But they also challenge the US Food and Drug Administration (FDA) to modernize its approach to evaluating new innovations.”

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  • How Artificial Intelligence is Changing Radiology, Pathology
  • Are Clinical Trials Diverse Enough to Ensure Patient Safety?
  • The FDA is looking to enhance innovation in areas that currently lack regulatory standards, including the field of artificial intelligence (AI).

    “AI holds enormous promise for the future of medicine. We’re actively developing a new regulatory framework to promote innovation in this space and support the use of AI-based technologies,” Gottlieb said.

    Black Charm Black Faux Dangle White and Rectangular with Fur Handbag “We know that to support the widespread adoption of AI tools, we need patients and providers to understand the connection between decision-making in traditional health care settings and the use of these advanced technologies.”

    To support AI adoption, the FDA is exploring how it can benchmark the performance of AI technologies in the field of radiogenomics, where algorithms can learn to correlate features on an MRI or PET scan with genomic features of tumors.

    The FDA is looking to collect large annotated imaging datasets16 London Conkca purse RFID card handcrafted leather Red 'Fey' pqUqSX to test the performance of AI algorithms. This would enable a transparent benchmarking system and help providers and payers compare the performance of an AI system to that of a human clinician.

    To further advance the role of AI in healthcare, the FDA is increasingly turning to patient-reported outcomes and wearable device data to provide insight into how patients will respond to this technology in controlled clinical and real-world settings.

    The FDA is also focusing on improving the efficiency of clinical trials, which are typically costly and complex to administer, Gottlieb said. Overly complex trials can be challenging for researchers, deter patient participation, and discourage the development of second- and third-to-market therapies, resulting in higher drug prices.

    To accelerate and modernize clinical trials, the FDA has created Master Clinical Trial Protocols (MAPs), which aim to increase trial efficiency and lower costs. These master protocols use common clinical trial infrastructure to streamline trial processes, enhance data collection and sharing, and improve data quality.

    In addition to accelerating the process of approving clinical drugs, the FDA is seeking to speed up the review of innovative medical devices. The organization recently proposed the idea of working interactively with sponsors of breakthrough devices to address regulatory challenges within a shorter amount of time.

    “These early interactions have resulted in the development of flexible clinical study designs for certain breakthrough devices and in more FDA review team support and senior management engagement earlier in the development and review process,” Gottlieb said.

    The FDA is also accelerating the process of reviewing drug safety. The new process will fully leverage the standard datasets that must be submitted in drug applications and will add data analytics and programming expertise to support medical teams’ safety analyses.

    Additionally, the agency is actively looking to use real-world evidence (RWE) to support regulatory decisions, including data from sources such as electronic health records (EHRs), registries, and claims and billing data.

    “Real world evidence can help answer questions that are relevant to broader patient populations or treatment settings where information may not be captured through traditional clinical trials,” Gottlieb wrote.

    The FDA has provided critical funding to examine strategies for generating and applying RWE to product development.In one proof-of-concept study, the FDA is funding a project that will investigate whether RWE generated using observational data can replicate the results of about 30 randomized controlled clinical trials for drugs.

    The healthcare industry is rapidly advancing, and innovative technologies will present new opportunities for medical care.

    “To facilitate these opportunities, and help make sure these innovations are able to improve public health, we’ve undertaken a comprehensive effort to make sure that our organization and policies are as modern as the technologies we’re being asked to evaluate, and that we’re able to efficiently advance safe, effective new innovations,” Gottlieb concluded.

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