Insights on AI, medical research, and the future of scientific discovery.
AI-designed molecules have entered clinical trials. AI-predicted protein structures have transformed structural biology. But the evidence for AI's impact on drug discovery success rates is more complicated than the headlines suggest.
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Why we built NousLab, and what we believe researchers actually need.
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Reading papers is not the same as extracting usable data from them. Most literature reviews produce summaries when they should produce structured datasets.
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Most clinical trial failures are not failures of execution. They are failures of design that were locked in before the first patient was enrolled.
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Most teams use AI to find papers faster. That is the least interesting thing it can do. Here is what changes when you treat it as a research infrastructure.
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Every researcher learns about bias in methods training. Most learn a list of names. What is harder to teach is how to recognise them in your own work before peer review does it for you.
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A rapid review done poorly is worse than no review at all. It gives false confidence in conclusions drawn from incomplete evidence. Here is how to do it properly.
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Real-world evidence is being used to support regulatory decisions in ways that would have been unthinkable a decade ago. That is either exciting or alarming, depending on how well you understand its limits.
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Grant reviewers read hundreds of applications. The ones that land are not the most ambitious ones. They are the ones that answer the right question in the right way.
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The replication crisis is not really a crisis of fraud. It is a crisis of incomplete evidence at the design stage, and that is a problem we can actually fix.
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We have had the same conversation more times than we can count. A researcher tried a tool, stopped using it within weeks, and can explain exactly why. The explanations are remarkably consistent.
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Accuracy is necessary. It is not sufficient. The most dangerous AI systems in medicine are not the ones that are wrong, they are the ones that are wrong with confidence.
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