biologit News

Recent advances in artificial intelligence applied to biomedical text are opening exciting opportunities for improving pharmacovigilance activities currently burdened by the ever growing volumes of real world data. To fully realize these opportunities, existing regulatory guidance and industry best practices should be taken into consideration in order to increase the overall trustworthiness of the system and enable broader adoption. In this paper we present a case study on how to operationalize existing guidance for validated AI systems in pharmacovigilance focusing on the specific task of medical literature monitoring (MLM) of adverse events from the scientific literature. We describe an AI system designed with the goal of reducing effort in MLM activities built in close collaboration with subject matter experts and considering guidance for validated systems in pharmacovigilance and AI transparency. In particular we make use of public disclosures as a useful risk control measure to mitigate system misuse and earn user trust. In addition we present experimental results showing the system can significantly remove screening effort while maintaining high levels of recall (filtering 55% of irrelevant articles on average, for a target recall of 0.99 on suspected adverse articles) and provide a robust method for tuning the desired recall to suit a particular risk profile.

Biologit is a sponsor at the 2021 World Drug Safety Congress Europe (October 6/7 in Amsterdam) and will present a talk on AI for medical literature screening. Looking forward to seeing you there!

Biologit was featured in Silicon Republic's Automation Week with an article with co-founder Nicole Baker and how automation will become increasingly important for screening scientific literature.

Big Ideas 2020 is Enterprise Ireland's annual showcase of investor-ready startups, taking place online on 25 November. biologit is one of the featured startups presenting pitches to Ireland's investment community.

The biologit team in collaboration with Trinity College Dublin presented experimental results on the use of AI for reducing medical literature screening workloads while maintaining high quality of results.