What Machine Learning Really Delivers in Literature Surveillance

 

The volume of published medical literature does not pause for pharmacovigilance teams. PubMed adds thousands of records daily, EMBASE adds more, and the regulatory mandate to screen this voluminous literature does not budge. A pharmacovigilance team that manually screens literature is not reviewing safety data. It is reviewing noise, looking for the fraction of it that is safety data.

This is precisely what ML in literature surveillance is designed to address. The question is not whether ML helps, but whether the way most teams implement it actually solves the right parts of the problem.

The argument for ML in pharmacovigilance literature surveillance is often framed around speed. That framing is not wrong, but it is incomplete. Speed matters less if it comes without accuracy. And accuracy without systematic noise reduction does not reduce reviewer burden.

What PV teams actually need is a connected set of ML capabilities that address the real failure points in the pipeline: volume management, ICSR detection quality, duplicate proliferation, and downstream data transfer.

This article examines what ML actually delivers across each of those areas, where the limitations are, and what an effective literature automation implementation looks like in practice.

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