Evidence synthesis and literature reviews are critical to the life sciences, but don't need to be critically painful.
Evidence synthesis is evolving, and nowhere is the need for speed and accuracy more pressing than in the life sciences domain, where the stakes are higher than ever to develop and deliver quality treatments to individuals worldwide. Whether you're conducting a systematic review, a scoping study, or a rapid review, researchers are under growing pressure to process more data in less time. With the rise of generative and agentic artificial intelligence technologies, it's now possible to automate the most time-consuming steps in the review process while keeping scientific oversight front and center. This guide explores how to use AI to accelerate evidence synthesis workflows in 2025 without compromising quality or intellectual integrity.
If you're a life sciences expert, you may already be familiar with the core principles of evidence synthesis (and if so, feel free to skip ahead to our AI-powered workflow using this link). But if you're new to the process, or looking to modernize how your team handles research reviews. In that case, this guide outlines best practices for conducting high-quality, AI-augmented evidence synthesis in a fraction of the time.
The Fundamentals Of Evidence Synthesis
Evidence synthesis refers to the structured collection, evaluation, and integration of existing research on a given topic. This process involves researching clinical guidelines, regulatory submissions, health technology assessments, and strategic research decisions in the life sciences.
Systematic literature reviews (SLR) are a well-known form of evidence synthesis and are a rigorous method for collecting, evaluating, and synthesizing all relevant studies on a specific research question. However, SLRs are just one approach to evidence synthesis. Researchers also utilize evidence synthesis in scoping reviews, rapid reviews, and meta-analyses. Regardless of the methodology employed, the goals are often similar: researchers want to conduct unbiased and precise synthesis of what is already known within the scientific community.
Simply put, SLRs are high-level research questions, divided between two or even three authors, that form the foundation of evidence-backed life sciences decisions. Done manually, this process is time-consuming, repetitive, and increasingly unsustainable in an industry that demands faster, data-driven insights.
Why Evidence Synthesis Consumes Endless Time
Despite its importance, evidence synthesis demands significant time and expertise. Teams spend days (and more often than not, weeks) manually:
- Searching databases
- Reviewing titles and abstracts
- Pulling and screening full-texts
- Extracting structured data into clean, analysis-ready tables
- Cross-validating decisions
This manual effort can’t keep pace with the growing volume of published research. For organizations competing on speed-to-insight, a manual-only approach isn’t scalable.
Why Use AI Tools for Evidence Synthesis?
AI doesn’t replace researchers—it helps them scale.
In an era when professional services are so often distinguished by the speed and quality of insights they bring to market, the importance and cost of life science discovery necessitate artificial intelligence augmentation. With the help of AI, expert researchers and strategists can focus on what they do best: providing quality insights at speed without sacrificing insight or integrity.
With the right tools, AI can help the team move beyond bottlenecks through:
- Cutting research time-to-completion by up to 80%
- Improving recall and precision within evidence gathering
- Freeing up scientific experts to focus on interpretation
- Building AI literacy on high agency and impact teams
Life science experts can accelerate evidence synthesis with the right platform while maintaining methodological integrity.
Step-by-Step: AI-Accelerated Evidence Synthesis Workflow
1. Generative AI as your PICOS Sounding Board
PICOS helps frame clinical questions (and SLRs). It stands for:
- P: Population
- I: Intervention
- C: Comparator
- O: Outcomes
- S: Study design
Generative AI can aid in specificity when detecting ambiguity or inconsistency in PICOS definitions.
You can use generative AI to refine vague criteria when structuring your clinical questions. If you were studying Crohn's Disease in older adults, use AI to phrase population requirements best.
For instance, instead of "older adults with Crohn’s," AI might recommend "adults aged 50–75 with Crohn’s Disease and no co-morbid diagnosis."
Additionally, Generative AI can detect inconsistencies early (for example, a mismatched population or intervention type). Finally, AI can pre-check criteria against existing studies to validate feasibility.
AI can also be a great way to pre-validate ideas with existing studies. This means that AI can quickly compare your research question or criteria with a vast database of existing studies, saving you the time and effort of manually checking each one. When done at scale, this can significantly reduce the time spent on literature review and provide a solid foundation for your research.
2. Smart Literature Discovery Using Agentic AI
Title and abstract screening is often the moment where scale meets brainpower and, historically, where time tends to stretch. Instead of manually constructing and refining search strings, agentic AI tools can rapidly surface the most relevant studies, learning from your inclusion/exclusion decisions.
With Reliant, your expertise still guides the SLR process, but the product removes the noise. In a case study reviewing over 3,000 oncology abstracts, Reliant achieved 99.9% recall and 92% precision, outperforming traditional language models and human-only workflows. That means fewer missed studies, fewer false positives, and more confidence in what you're including.
3. AI-Augmented Screening and Adjudication
Title and abstract screening is the biggest time cost in evidence synthesis. Manually reviewing thousands of records is tedious and clashes with the attention to detail required from experts. Reliant streamlines this process with AI trained on domain-specific data, learning from your decisions to surface relevant studies first, flag uncertain ones, and keep a clear audit trail.
Studies show AI-assisted screening cuts timelines from 2–3 weeks to just 2–3 days, maintaining over 95% accuracy and sometimes even improving recall over manual methods. The result is higher productivity for consultants and researchers, who can then produce more comprehensive reviews in less time.
4. From Text to Tables: Structuring Data at Scale
Generative AI often has two outputs: conversational (which you may be familiar with if you've used ChatGPT or Gemini) and Tabular (which takes the form of analysis-ready tables). The latter transforms unstructured data (text and figures) into structured insights. Traditionally, this means pulling outcomes, sample sizes, interventions, and endpoints manually—usually into a table—study-by-study.
When you partner with Reliant, structured data is automatically extracted, mapped to consistent terminology (e.g., MeSH, MedDRA), and dropped directly into clean, analysis-ready tables: no formatting gymnastics or inconsistent variable names. Users can also use the platform to export tables directly or drop them into visualizations.
5. Why Human Oversight Still Matters in AI-Assisted Reviews
Reliant is designed to augment human expertise, not replace it.
While AI accelerates screening, extraction, and structuring, critical thinking and scientific judgment remain firmly in human hands. The system supports, learns from, and adapts to expert reviewers, creating a feedback loop where humans guide the AI and the AI scales its impact.
The marriage of machine learning and the life sciences is the foundation for collaborative intelligence, which is built to help researchers spend less time on repetitive tasks and more time delivering insight.
Can Reliant Improve my Evidence Synthesis Process?
Yes! If you're unsure how to augment your work with AI just yet—be it for security reasons or trepidation around new technology—Reliant's experts are consultants trained in the life sciences. They are here to help set up AI that works for your team's specific goals.
Curious how AI can accelerate your next review or evidence synthesis project? Book a demo with Reliant using the form below to see how you can use AI to augment your evidence synthesis needs—securely and scientifically.
