Interpretability is crucial because it allows researchers and clinicians to:
Validate the accuracy and reproducibility of analytical methods. Ensure the consistency and reliability of biomarker measurements. Identify and correct potential errors or biases in data analysis. Translate analytical data into actionable insights for therapeutic and diagnostic purposes.
Without interpretability, the data generated from bioanalytical methods would be meaningless, hindering progress in biomedical research and clinical practice.
