Deep-Learning Models Meet Medical Literature AI-assisted Literature Search and Review. Real World Data. Actionable Insights.

Key challenges

Literature Monitoring and Review is becoming increasingly important as an input into the efficacy and safety of medicines, cosmetics, and medical devices. In some geographies, the companies (Marketing Authorisation Holders) are expected to monitor medical journals and databases on a weekly basis and extract applicable adverse events. Literature Review Summary is an expected inclusion in Periodic Safety Reports and Clinical Evaluation Reports (CERs) for Devices. Most life sciences companies also have tons of internal documents and data sets that could be used as part of a Systematic Literature Review (SLR) or Meta-Analysis or Literature Monitoring for Pharmacovigilance. Literature Search and Reviews are traditionally a manual process.

Existing process is prone to bias, time consuming and susceptible to human error as the reviewer is focused on the manual process and not the analysis of information.

No end-to-end solution in the market that allows literature search and monitoring, review of full articles, and generation of Summary reports and analytics.

Apart from regulatory compliance, Life Sciences companies are also looking for ways to extract valuable insights from the process.

Large amounts of unstructured and inaccessible internal documentation.

With vast amount of research and associated literature being produced, it is easy to miss content produced by new research.