As the demands for faster, more accurate, and scalable literature reviews rise in life sciences, AI is no longer just a future promise—it’s a proven performance driver. Systematic literature review (SLR) teams, especially in HEOR and medical affairs, are adopting GenAI platforms not just for experimentation but to solve long-standing bottlenecks in screening, extraction, and synthesis.
So, what does success look like when AI is put into action? The following case studies, drawn from leading pharmaceutical companies across oncology, rare diseases, and real-world evidence reviews, provide hard evidence of the time savings, accuracy, and scalability GenAI solutions are already delivering.
Case Study 1: First-Line NSCLC Screening in 8 Hours, 95% Accuracy
A leading oncology team sought to accelerate the primary screening for PD-L1+ NSCLC treatments. Using a GenAI-powered platform with machine-tagged PICOS and instructional prompts, the client was able to process 200+ clinical articles in just 8 hours—a task that typically takes days to weeks.
- Accuracy achieved: 95.5%
- Exclusion sensitivity: 95.98%
- Time saved: Estimated 80% reduction in manual effort
The platform not only handled deduplication and title/abstract screening seamlessly, but its high precision drastically reduced reviewer fatigue and improved downstream screening efficiency.
Case Study 2: HEOR Customization Achieves 86% Extraction Accuracy
A global pharmaceutical HEOR team wanted customized data fields focused on post-bleeding treatment patterns and cardiovascular risk extraction. Starting with GPT-4 Turbo and later upgrading to GPT-4o for text + table extraction, the team improved extraction accuracy from 50% to 86% during pilot evaluations.
This marked a turning point for scalable HEOR customization, proving that with iterative tuning and human feedback, AI can deliver near-expert level precision in extracting highly specific clinical insights.
Case Study 3: 37 Full-Text Articles Reviewed for mCRPC in Record Time
A systematic review of real-world mCRPC treatment patterns—traditionally a months-long task—was completed with a robust AI workflow:
- Initial corpus: 3,656 publications + 896 abstracts
- After title and abstract (T&A) screening: 118 relevant
- Full-text relevant: 37 confirmed
- Final output: Manuscript-ready, quality-checked, and based on real-world data across regions
Beyond speed, the AI-supported SLR helped reveal treatment sequences and duration trends that aligned closely with current clinical practices, creating both clinical and commercial value.
Client Spotlight: Before vs. After AI Adoption
Across all these case studies, one key trend stands out: teams that previously relied solely on manual or semi-automated processes are now completing reviews 2–4x faster and with significantly better traceability. Tasks that once required weeks of effort from cross-functional teams are now being completed in days, without sacrificing quality or compliance.
Before adoption:
- Fragmented tools and workflows
- Bottlenecks during screening and extraction
- Inconsistent reviewer agreement and traceability
After adoption:
- Integrated, AI-assisted workflows
- Faster screening (up to 90% time savings)
- Higher inter-reviewer alignment and confidence in outputs
Industry Benchmarking: Speed, Accuracy, and IRR
When benchmarked against traditional norms, AI platforms are now:
- Screening titles/abstracts 40–60% faster
- Achieving accuracy levels of 85–95%
- Improving inter-reviewer agreement (IRR) by providing standardized AI-supported outputs
This is especially critical in pharma settings where reproducibility and auditability are non-negotiable.
Conclusion: From Promise to Proven
AI is no longer a theoretical advantage—it’s a practical one. As these real-world examples show, GenAI-enabled platforms are redefining the pace and quality of evidence synthesis. From early screening to customized HEOR extraction and MAS generation, the results speak for themselves.
For medical affairs, HEOR, and regulatory teams evaluating AI vendors, the lesson is clear: focus not just on features, but on demonstrated outcomes—speed, accuracy, transparency, and scalability.
Real reviews. Real results. Real impact.
