Insights on AI, medical research, and the future of scientific discovery.
Precision medicine has transformed cancer treatment in specific contexts. In most common diseases, the promised era of molecularly tailored therapy has not arrived - and the reasons are structural, not just technical.
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Subgroup analyses are statistically unreliable, frequently misleading, and clinically irresistible. Understanding why they produce false positives - and when they can be trusted - is essential for reading trial results critically.
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Free-text clinical notes contain information that structured EHR fields never capture. Natural language processing can extract much of it - but the gaps, especially around negation and clinical context, are larger than most implementations acknowledge.
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Peer review is slow, inconsistent, and under strain from a publishing volume that has grown faster than the reviewer pool. The reforms being proposed are varied, and not all of them address the actual problems.
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Every sample size calculation is built on assumptions about effect size, variance, and dropout - and most of those assumptions are optimistic. Underpowered trials are not bad luck. They are a predictable consequence of how power calculations are done.
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Scoping reviews and systematic reviews are not interchangeable tools. Choosing the wrong one wastes resources at best and produces misleading conclusions at worst - and the misuse of scoping reviews to avoid rigour is a real pattern.
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The NIH now requires data sharing plans for most funded research. The "Nelson memo" changed federal open access policy overnight. The direction of travel is clear - but the tension with commercial interests in pharma research is real and unresolved.
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Network meta-analysis can rank every treatment in a therapeutic area from a single analysis - even when most of them have never been tested head-to-head. The method is powerful, and the assumptions it rests on are frequently overlooked.
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Patient-reported outcomes are not just quality-of-life questionnaires appended to trials as afterthoughts. They capture dimensions of treatment benefit and harm that clinicians systematically miss - and regulators are starting to demand them.
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Tumour shrinkage is not survival. Blood pressure reduction is not stroke prevention. The use of surrogate endpoints in clinical trials has accelerated drug approval - and produced some of medicine's most expensive mistakes.
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HARKing - presenting post-hoc hypotheses as if they were a priori - is widespread, rarely acknowledged, and one of the most direct contributors to the replication crisis. It is also not discussed honestly enough in methods training.
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Drug repurposing has produced some of medicine's most useful treatments. AI is accelerating the identification of candidates - but the gap between computational signal and clinical validation remains wide.
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