Could medical research be more adventurous?

An article last month on the Nautilus website posed an interesting question: “Why is Biomedical Research So Conservative?” Possible answers were summed up in the sub-headline: “Funding, incentives, and scepticism of theory make some scientists play it safe.” This is not to say that there is insufficient imagination going into the research application of advances in machine learning and data management in improving health outcomes. In fact, another article came out about the same time on the Fast Company website, discussing “How Artificial Intelligence is Bringing us Smarter Medicine”. It distinguished a host of impressive advances under five significant headings: drug creation, diagnosing disease, medication management, health assistance, and precision medicine.

People could say ah yes: well, that’s machine smarts at the applied rather than the theoretical end of medical research, although that is less true in the first and last of these categories: supercomputers are very much engaged in the analysis of molecular structures from which it is hoped new therapies will emerge, and in the vast data sets which are being created within the science of genomics as we move into a new era of precision, personalised medicine.

Nevertheless, it does seem that pure research in biology – as distinct from physics – has been playing it comparatively safe, and the Nautilus article provides the evidence for its ruminations. Natural language processing analysis of no fewer than two million research papers, and 300,000 patents arising from work with 30,000 molecules, showed a remarkable bias towards conditions common in the developed world and with an emphasis on areas where the research roads are already well travelled – predominantly in cancer and heart disease.

My own communications work in the dementia environment – specifically Alzheimer’s disease – suggests that another reason may be in play. Where medical research has been less conservative, more adventurous, and broadly more successful, there has been more collaboration and shared excitement around a commonly perceived mission. The more open-source, zealous, and entrepreneurial eco-system that has applied for decades in heart and in cancer research – and we see this now on steroids in the field of artificial intelligence developments – has yet to capture the imagination of the wider biomedical community, where the approach remains generally more careful, more academic and inward-looking: just, more conservative.

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