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Personalized medicine uses patient data for tailored treatments, but data science models need improvement for clinical use. Overcoming challenges requires interdisciplinary collaboration and advanced computational methods for real-world impact.

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Area of Science:

  • Data Science
  • Biomedical Informatics
  • Computational Biology

Background:

  • Personalized medicine stratifies patients using individual characteristics and biomarkers.
  • It relies heavily on data science and machine learning (AI).
  • Current clinical practice adoption is limited due to model performance and validation issues.

Purpose of the Study:

  • To review state-of-the-art data science for personalized medicine.
  • To discuss challenges hindering clinical implementation.
  • To highlight future research directions.

Main Methods:

  • Review of current data science approaches in personalized medicine.
  • Analysis of challenges in predictive model performance and interpretability.
  • Exploration of validation requirements through prospective clinical trials.

Main Results:

  • Despite enthusiasm for 'big data' and AI, few solutions impact clinical practice.
  • Key barriers include insufficient model performance, interpretability issues, and lack of demonstrated clinical benefit.
  • Future progress depends on addressing these limitations.

Conclusions:

  • An interdisciplinary effort is crucial, involving data scientists, clinicians, and regulators.
  • Managing expectations and concerns regarding data science is necessary.
  • Advancing computational methods is essential for direct clinical benefit.