Diagnosing rare disease patients is inherently difficult. Because individuals with rare diseases are diffused geographically, exhibit different symptoms across the same disease, and are often attended by specialists with varying expertise in rare diseases, this leads to insufficient data and knowledge accumulation, leading to challenges with both clinical care and research.
Pairing connected real-world data and artificial intelligence promises to accelerate the path to diagnosis for rare diseases. By training AI models on large volumes of aggregated, de-identified rare disease data, researchers are able to glean critical insights that a human eye or manual scouring of data cannot detect.
In this co-authored article, Ambit and Datavant break down:
- How AI can accelerate diagnosis and improve outcomes for rare disease patients
- The importance of fit-for-purpose, privacy-preserving data for training AI models
- The impact of stakeholder alignment on the rare disease community
Read the article, How AI and Real-World Data Can Improve the Lives of Patients With Rare Diseases.
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