Episode Details

The FDA recently released a framework detailing how to use digital health technologies and AI to capture clinical trial outcomes. We explore the critical differences between verification, analytical validation, and clinical validation, and what trial sponsors must do to prove a digital measure is fit for purpose.

Key points

• Digital health technologies (DHTs) can capture continuous, real-world data outside clinical settings, creating what the FDA calls digitally derived measures (DDMs).
• Developers must clearly define the context of use and ensure the DDM captures a meaningful aspect of health, heavily involving patient and caregiver input early on.
• The FDA requires a rigorous, multi-step process: verifying the sensor works, analytically validating the algorithm measures the right metric, and clinically validating that the metric tracks the disease.
• Usability studies are critical; devices must accommodate the sensory, cognitive, and motor capabilities of the specific target population.
• Sources of error like environmental factors, incorrect device placement, or operating system updates must be continuously managed to ensure the DDM remains fit-for-purpose.

Source: Key Considerations for the Development and Use of Digitally Derived Measures for Clinical Investigations - U.S. Food and Drug Administration, 2026

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This episode is an AI-generated conversation summarising a public document; the hosts' voices are synthetic. It is for information only and is not medical advice. Always refer to the original source.

Full transcript: https://ai-in-medicine-podcast.vercel.app/episodes/digital-measures-in-trials-unpacking-the-fda-s-new-framework-2b821f