A new article published in Applied Clinical Trials Magazine, authored by Mats Sundgren, Rohit Mistry, and Gabriel Maeztu, delves into the groundbreaking potential of multimodal AI platforms and natural language processing (NLP) in revolutionizing clinical research.

The article, titled “Harnessing Unstructured Data and Hospital Interoperability,” addresses a critical challenge: over 80% of healthcare data is unstructured and often overlooked in traditional trial processes. By leveraging advanced NLP and standards like OMOP CDM, IOMED’s technology transforms fragmented data into standardized, actionable insights.

Key highlights from the article include:

• Enhanced patient identification, improving trial efficiency and personalization.
• Accelerated recruitment timelines and optimized trial design.
• Recent studies show a 20% increase in eligible patient identification, reducing costs and delays.

The publication also emphasizes the broader implications of these innovations, aligning with major initiatives like the European Health Data Space (EHDS) and promoting personalized medicine and real-world evidence (RWE) generation.

“This article underscores the transformative role of AI and data interoperability in advancing clinical research. By unlocking unstructured data, we’re not just improving trials; we’re reshaping the future of healthcare.” said Rohit Mistry.

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