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Intelligent Literature Monitoring (ILM) with Integrated Artificial Intelligence Case Study

Transforming literature search and publication planning 

 

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.

Download the ILM Case Study to learn more. 

 

Intelligent Literature Monitoring Demo Video

Monitor scientific content at scale and drive Medical Affairs strategy with clear insight.  Language Intelligence features 100% AI-powered clinical auto-tagging, document comparison in real-time, auto-summarization of abstracts/sections, biomedical concept filters, and insights from tailored clinical ontologies. Watch the demo video to see it in action.
 
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