From hype to reality: data science enabling personalized medicine

TLDR

Personalized medicine stratifies patients by disease subtype, risk, prognosis, or treatment response using specialized diagnostics and relies on data science and machine learning, yet its clinical impact remains limited because of model performance, interpretability, and a lack of prospective validation. The study reviews the potential of state‑of‑the‑art data science approaches for personalized medicine, discusses open challenges, and highlights directions to overcome them. The authors synthesize current literature on data science methods applied to personalized medicine, evaluating their strengths and limitations.

Abstract

Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future.