What is Evidence?

In scientific research, evidence is the bedrock of all credible knowledge. It is built on empirical data and observations, meticulously gathered through experiments, measurements, and analyses. But for evidence to hold scientific weight, it must be reliable, reproducible, and free from bias.

Over the past 50 years, the process of generating evidence has evolved dramatically—especially in medicine. Here, accuracy isn’t just a target; it’s an absolute necessity. Mistakes in medical research can have serious consequences, which is why the field enforces some of the most rigorous scientific standards to guarantee reliable results.

In many research fields, there’s often a trade-off between speed and precision. But in medicine, minimizing risk and bias isn’t optional—it’s vital to protecting patients.

Evidence-Based Medicine: When Accuracy Takes Priority Over Speed

Evidence-Based Medicine (EBM) provides the methodological foundation that ensures healthcare decisions are made based on the best available scientific evidence, rather than personal experiences or anecdotal knowledge.

One of the greatest challenges in research is bias—systematic errors, that can distort results. Bias can arise in various ways, including:

  • Poorly designed studies that fail to account for sources of error
  • Selective data reporting, where only the most favorable results are published
  • Conflicts of interest, where financial or institutional pressures influence conclusions

To counter these risks, EBM relies on structured methodologies, such as randomized controlled trials, systematic literature reviews, and meta-analyses. These methods ensure that medical decisions are based on robust, objective data rather than intuition or isolated studies.

However, while EBM has led to significant advancements in medical science, it is also time-consuming. Reviewing, analyzing, and synthesizing scientific studies can take months or even years, delaying both research progress and access to new treatments.

AI: A Solution to Accelerate Evidence Generation

The rigorous methodology of EBM cannot be compromised—but its processes can be optimized. This is where artificial intelligence (AI) comes in as a tool that can accelerate evidence-based analyses without lowering scientific standards.

The EVA project is working to integrate AI into evidence-based medicine to speed up key processes such as:

  • Screening scientific literature, where AI can rapidly identify relevant studies
  • Automated data extraction, ensuring high precision in collecting research findings
  • Efficient evidence synthesis, where AI systematically compares and aggregates large datasets

However, AI is not just about increasing speed—it is also about enhancing research quality. With the growing volume of scientific publications, it is no longer practical to stay fully updated through manual review alone. The EVA project addresses this challenge by combining AI’s computational power with EBM’s methodological rigor.

This approach ensures that:

  • New research can be integrated into clinical practice faster
  • Medical decisions are based on a stronger knowledge foundation
  • Patients gain quicker access to new and improved treatments

By leveraging AI without compromising scientific integrity, the EVA project is paving the way for a new era in medical research—where evidence is produced faster, more systematically, and with greater precision.