Challenge

In the pharmaceutical industry, medical affairs teams are responsible for creating high-quality, evidence-based documents that demonstrate the safety and efficacy of new treatments. This requires time-intensive tasks such as:

• Conducting literature database searches

• Screening abstracts for relevance

• Extracting metadata (authors, journals, dates)

• Summarizing and structuring scientific findings

• Ensuring every claim is correctly referenced

Creating a single coherent narrative from fragmented sources could take several days of manual work, especially when tailoring the content to diverse audiences, from clinicians and researchers to patients and stakeholders.

Solution

Common Sense AI developed a generative AI solution to automate the end-to-end workflow of evidence generation for medical affairs teams.

Built on large language model (LLM) technology, the solution supports:

• Automated generation of search terms for literature databases

• Abstract screening and filtering for relevance

• Metadata extraction from publications

• Drafting of scientifically accurate, well-structured content

• On-demand answers to targeted medical questions, with references

By integrating domain-specific knowledge into the AI pipeline, the tool transforms raw literature inputs into a polished, readable, and referenced draft, ready for review and final editing.

Result

The impact was substantial:

• Time savings: what previously took several days was now achievable in minutes

• Greater consistency in scientific writing and referencing

• More strategic focus for medical teams, shifting effort from manual processing to high-value analysis

• Improved responsiveness, with on-demand generation of summaries, comparisons, and claims across therapeutic areas

This project shows how generative AI can transform medical content creation, accelerating workflows, increasing accuracy, and supporting faster, more informed communication across the pharmaceutical sector.