Pharmaceutical research depends on a steady flow of evidence, from the earliest hypotheses to the real-world studies that validate them. Yet one of the most foundational steps in this process, the systematic literature review (SLR), often becomes a roadblock instead of a launchpad. Faced with millions of new publications each year and the variability of clinical language, research teams are spending more time wrangling data than generating insight.
That’s changing fast. By pairing expert-driven artificial intelligence (AI) with standardized clinical terminology, pharmaceutical teams are achieving 27x faster literature reviews with 94% accuracy — all while maintaining the transparency and reproducibility that regulatory-grade evidence demands.
We explore this in our latest insight brief: Outsmarting data bottlenecks in pharma: The clinical terminology advantage. Through real-world use cases, including a systematic literature review powered by GPT-4 and IMO Health’s curated terminology, we show how the right clinical foundation can turn one of a researcher’s most time-consuming tasks into a true accelerator for discovery.
INSIGHT BRIEF
Outsmarting data bottlenecks in pharma: The clinical terminology advantage
Here’s a look inside.
Systematic literature review
SLR is a structured and reproducible method of identifying, evaluating, and synthesizing relevant studies on a specific research question. Whether supporting new drug development, assessing adverse events and economic impacts, or guiding early-stage research, SLR is essential for informed, scientifically sound decision-making in healthcare and biomedical innovation. Yet despite the importance of this task to advance life sciences research, the process can be cumbersome, inefficient, and prone to human error due to a number of factors.
- The volume of biomedical literature: The number of biomedical publications is growing rapidly, with over 1.5 million new citations added to PubMed in 2023 alone. As a result, the manual review of publications is increasingly time consuming – and costly – as reviewers are typically highly skilled and in high demand.
- Variability in clinical language: Clinical concepts and outcomes can be described using a variety of terms (think: emphysema, COPD, chronic bronchitis). This variability can result in inconsistency and ambiguity as data is synthesized and interpreted.
- Bias and human error: Individuals who do manual literature review are susceptible to cognitive biases that can lead to inconsistent decision-making, reduced reproducibility, and the potential for inadvertently excluding relevant studies.
- A lack of trust in AI: While leveraging AI has the potential to greatly accelerate the SLR process, without transparent methods and standardized clinical terminology underpinning these algorithms, AI-driven reviews risk introducing new forms of bias, missing critical studies, or delivering outputs that can’t be validated or reproduced.
The IMO Health approach
To address the inefficiencies of traditional literature reviews, IMO Precision Literature Review combines expert-grade AI with validated clinical terminology to streamline article screening and data extraction. In one recent example, using OpenAI’s GPT-4, the solution automated key steps of the literature review process – abstract screening, full-text screening, and structured data extraction – achieving faster, more accurate evidence generation for endometrial cancer research.
Results and impact
- 94% identification accuracy for relevant studies, closely matching manual search outcomes.
- 27x faster processing time, significantly reducing review timelines and resource demands, all while including human-in-the-loop feedback.
