From hype to reality: data science enabling personalized medicine.

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.