Systematic Literature Reviews (SLRs) are a cornerstone of evidence-based research, providing comprehensive and unbiased insights across scientific disciplines. However, conducting an SLR is a labor-intensive and time-consuming process, requiring researchers to sift through thousands of abstracts manually to identify relevant studies.

Reliant AI offers a transformative solution, dramatically improving the efficiency and accuracy of SLR workflows. 

Challenges in SLR

Accuracy Trade-offs

Commercially available LLMs achieve an average recall of 88% and precision of only 65% in targeted life sciences questions, leading to missed relevant studies and false positives. In contract, human analysts working in pairs can achieve 95% recall and precision, but at the cost of significant manual effort.

Time-Intense Workflows

Even experienced researchers take weeks or months to complete an SLR due to the sheer volume of literature.

Data Privacy Concerns

Many AI tools require user interactions and client data for continuous learning, raising concerns about data security and confidentiality.

Reliant Tabular for Literature Reviews

In a case study analyzing 3,000 oncology abstracts, Reliant Tabular achieved a 99.9% recall and 92% precision, surpassing both human analysts and commercially available LLMs. This ensures researchers do not miss critical studies whiel minimizing irrelevant inclusions. Together with Reliant AI human experts can process literature 200 times faster than on their own, significantly reducing the time required to complete a literature review. Read more in our white paper on SLR to learn how to balance the trade-off between recall and precision in literature search and how Reliant Tabular can help you strike the perfect balance.