As AI shifts the way we do biomedical research from discovering new drugs to tailoring treatments, it’s becoming clear that not all data is created equal. While the old mantra “bigger is better” may have worked for data hoarders of the past, modern AI reminds us that it’s not how much data you have, but how good the data is, that makes all the difference.
“Not everything that can be counted counts, and not everything that counts can be counted.”
-Albert Einstein
The same applies to AI datasets. Larger datasets often come with hidden baggage noise, inconsistencies, and biases that derail AI models. On the flip side, high-quality data is a game changer. Harmonized, consistent, and error-free datasets enable AI to work smarter, not harder. Standardized variables and complete metadata ensure seamless integration across studies, making reproducibility not just possible but reliable. This level of precision is critical in biomedical AI, where even small errors can lead to costly mistakes. Moreover, robust data mitigates biases, empowering AI to perform equitably across diverse patient populations.
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