When striving to maintain a balance between subjective interpretations and objective findings, researchers often take the following measures into account:

The peer review system serves as a critical mechanism for assessing the quality of scientific research. While it aims to mitigate biases through methods such as:

• Review by multiple independent experts to ensure diverse perspectives and reduce individual bias.
• Verification of methodology and scientific rigour to uphold research standards, it can inadvertently introduce new forms of bias, including:
• Confirmation bias: A tendency among reviewers to favour research that aligns with their own viewpoints.
• Reputation bias: Articles authored by prominent researchers may receive preferential treatment, even when of comparable quality to those from lesser-known authors.

In light of these challenges, AI-driven tools can offer promising solutions for improving the balance between subjectivity and objectivity in research, through:

• Unbiased analysis of extensive datasets, which reduces the risk of human error and partiality.
• Detection of linguistic and methodological biases in research manuscripts, helping authors and reviewers address potential weaknesses.
• Automated evaluation of research quality and methodology, streamlining the peer review process and alleviating the workload on human reviewers.

Navigating the balance between subjectivity and objectivity in evidence-based science is crucial, and peer review plays a vital role in this process by providing critical evaluation and diverse perspectives on research findings. Meanwhile, AI tools can enhance the objectivity of assessments by analyzing data patterns and identifying biases, thereby supporting the peer review process and ensuring that scientific conclusions are robust, reproducible, and less influenced by individual subjectivities.