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biologit News

Biologit at the DIA Global conference, June 2023

Biologit at the DIA Global conference, June 2023

Join us booth at the DIA Global conference June 2023 to discover how biologit MLM-AI can help you streamline your literature monitoring processes.

Duplicate detection in biologit MLM-AI

Duplicate detection in biologit MLM-AI

We explore the impact of duplicates in literature searches and introduce biologit MLM-AI's automatic duplicate detection features

How Ireland is staying at the forefront of connecting AI to the life sciences industry.

How Ireland is staying at the forefront of connecting AI to the life sciences industry.

Biologit one of the Ireland-based tech companies leading AI in the life sciences, by Enterprise Ireland

Talk to us at the Pharmacovigilance Strategy Meeting USA - Boston May 16 2022

Talk to us at the Pharmacovigilance Strategy Meeting USA - Boston May 16 2022

Biologit is a sponsor at the Pharmacovigilance Strategy Meeting USA 2022 (May 16 in Boston). Looking forward to seeing you there!
VIP code - BIOL2022VIP

Validation and Transparency in AI systems for Pharmacovigilance: a case study applied to the medical literature monitoring of adverse events

Validation and Transparency in AI systems for Pharmacovigilance: a case study applied to the medical literature monitoring of adverse events

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.

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