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Creation of Scientific Response Documents for Addressing Product Medical Information Inquiries: Mixed Method Approach

Jerry Lau1,2,3, Shivani Bisht4, Robert Horton5

  • 1phactMI, Gainesville, FL, United States.

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|March 13, 2025
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Summary
This summary is machine-generated.

This study explored using AI and machine learning to improve the creation of scientific response documents (SRDs). Integrating these technologies can streamline content development and address challenges like paraphrasing, enhancing accuracy and efficiency for pharmaceutical information delivery.

Keywords:
AIGPTGenerative Pre-trained TransformerLLMSRDaccountabilityaccuracyartificial intelligencebenefitbiopharmaceuticalclinical datacontent analysiscontent generationcontent generatorcontextdevelopmentdocumentationfeasibilityinformationlarge language modelmachine learningmedical informationpharmaceuticalreferencescientific responsescientific response documentationstrategysurveytraceability

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Area of Science:

  • Pharmaceutical sciences
  • Medical informatics
  • Artificial intelligence in healthcare

Background:

  • Scientific Response Documents (SRDs) are crucial for communicating evidence-based medication and disease information to healthcare professionals.
  • Medical information departments face escalating demands and complexity in SRD development, prompting exploration of AI and large language models (LLMs).
  • Traditional SRD development involves time-intensive tasks like paraphrasing scientific articles, highlighting a need for efficiency improvements.

Purpose of the Study:

  • To quantify the challenges encountered in the development of SRDs.
  • To develop a framework for integrating AI capabilities into the SRD creation process.
  • To assess the feasibility and value addition of AI in generating concise SRD summaries.

Main Methods:

  • A survey of pharmaceutical companies (phactMI consortium) was conducted to assess SRD development challenges.
  • A working group utilized AI, including logistic regression and transformer-based summarization, for SRD authoring, focusing on data extraction and abstraction.
  • Machine learning models were trained on semantic embedding features for classification and summarization tasks.

Main Results:

  • Survey results indicated that paraphrasing scientific articles is the most tedious and time-consuming aspect of SRD development.
  • Machine learning models demonstrated high accuracy (ROC scores 0.67-0.85) in distinguishing target categories for data extraction.
  • Evaluating data abstraction using BLEU scores and semantic similarity revealed varied reviewer preferences, indicating trade-offs in summarization quality.

Conclusions:

  • A framework for integrating LLMs and machine learning in SRD development has been established.
  • Machine learning models show promise for section identification and content usability in data extraction and abstraction.
  • Further research and optimization are necessary for full-scale industry implementation of AI in pharmaceutical SRD development.