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Through seamless integration of fragmented clinical data, AI enhances clinical trials, ensuring greater safety, efficiency, and a focus on patient-centered outcomes.
The healthcare industry is swimming in data—think electronic health records, clinical trial platforms, lab results, wearables, and health apps. Roughly 30% of the world’s data comes from this sector, and it is growing fast, with a projected 36% annual increase by 2025, outpacing fields like finance or entertainment. The problem? This treasure trove of information is scattered across disconnected systems, stored in different formats, with mismatched metadata. The mess that results blocks the kind of big-picture insights needed to make clinical trials smoother, safer, and more effective.
This paper dives into how artificial intelligence (AI) is shaking things up by pulling this fragmented data together. AI doesn’t just crunch numbers—it can turn messy, freeform text into structured data, spot similarities between terms like ‘acetaminophen’ and ‘paracetamol,’ and even make sense of lab reports without needing a rigid template. It can summarize lengthy clinical notes, learn from past trials to predict issues, and even analyze images like tumor scans for patterns. This means fewer missed red flags, like adverse events, and better ways to find the right patients or trial sites.
By weaving all this data into a single, clear view, AI helps researchers work smarter, cutting down on repetitive tasks, catching risks early, and speeding up drug development. We’re talking about a future where clinical trials are more patient-focused and efficient, built on a digital ecosystem where data flows freely between stakeholders.
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