AI Article Synopsis

  • Precision Medicine shifts focus to patient-centered treatment by using predictive biomarkers to tailor therapies, minimizing ineffective treatments and side effects.
  • The rise of "multi-omics" and sensor technology enables extensive health data collection, fostering a comprehensive health management system for personalized real-time insights and preventive strategies.
  • Real-world data applications in translational research offer new methods beyond traditional evidence-based medicine, especially in areas like precision oncology, where data integration supports individualized cancer treatment planning.

Article Abstract

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8002976PMC
http://dx.doi.org/10.3390/jpm11030216DOI Listing

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