5 Key Takeaways

• 1

  Machine learning enhances vibrational spectroscopy but inconsistent analytical practices hinder clinical adoption more than model performance.

• 2

  The review highlights the importance of the entire analytical pipeline, including sample preparation and spectral acquisition.

• 3

  High classification accuracy in small datasets does not guarantee model effectiveness across different patients or clinical settings.

• 4

  Hybrid models combining machine learning with chemically meaningful inputs may improve predictions and their connection to biochemistry.

• 5

  Clinical progress relies on reproducibility, transparency, and evidence of reliable performance outside laboratory conditions.