Medistrava and Sorcero enable life science enterprises to extract valuable insights from an expansive number of sources in a centralized location, thereby accelerating the tedious manual review process, increasing productivity, and providing a deeper understanding of complex, scientific content.
Intelligent Literature Monitoring (ILM) is a novel approach to augmenting and running continuous systematic literature searches around defined areas of therapeutic domains. ILM leverages Artificial Intelligence (AI) and its subset discipline Natural Language Processing (NLP). This enables the extraction of valuable insights from an expansive number of medical publications, as they are being published, into a centralized location.
Objective:
Sorcero, MEDiSTRAVA, Coherus, and Moderna aimed to demonstrate the benefits of transforming literature monitoring from a manual report-based process into an intuitive, semi-automated, AI-driven process.
Methods:
We assessed two different approaches to continuous literature monitoring:
1. Manual search and output
2. Semi-automated search, with AI integration and digital output, applied across search scenarios producing high (>250) and low (<100) volumes of results.
ILM was performed using the Sorcero LI Platform and the BioBERT language model against identical corpora. We compared each approach for man-hours, sensitivity, specificity, versatility/utility of outputs, and depth of insights. We also qualitatively assessed the digital output from a user experience perspective.
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