Data Scientist Explains How AI's Seductive Power Can Mislead Biomarker Researchers

As regular readers know, I've been writing quite a bit over the last year about the opportunities and challenges associated with bringing advances in data and digital to bear on the discovery and development of impactful new medicines. I've been struck by the potential of many of these powerful approaches, tools, and techniques, but underwhelmed by the drooling that's often accompanied them. An important theme of this column has been that despite what seems like exceptional potential, the impact of data science and digital on drug discovery and development to date has been conspicuously limited. This may reflect the extravagant expectations around big data, which has become viewed as a self-evident religion (preached by managerialist consultants), rather than as a potentially useful tool that must rigorously prove itself in context, as I recently discussed. I've also examined the impact of cultural factors (and how the culture of data science differs from that of pharma), here; the challenge of AI black boxes, here; and the importance of understanding the difference between invention and implementation (here).