Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Area of Science:

• Biomedical Sciences
• Genomics
• Systems Medicine

Background:

• Traditional medicine's single-etiology approach is insufficient for complex diseases.
• Personalized Medicine (PM) offers a paradigm shift, but interpretations vary, especially concerning genetic and genomic factors.
• Lifestyle and environmental factors significantly influence complex conditions, complicating purely genetic-based PM.

Purpose of the Study:

• To explore the challenges and potential of personalizing clinical phenotyping for complex diseases.
• To investigate the role of various etiological factors beyond genetics in disease manifestation.
• To identify effective techniques and tools for advancing personalized medicine.

Main Methods:

• Review of scientific literature on Personalized Medicine and complex conditions.
• Analysis of practical experiences from a leading research hospital (Fondazione Policlinico Gemelli).
• Exploration of Artificial Intelligence (AI) applications within Systems Medicine.

Main Results:

• Complex diseases require a multi-faceted approach, integrating genetic, lifestyle, and environmental data.
• Personalizing clinical phenotyping is challenging due to diverse pathophysiological mechanisms.
• AI-powered Systems Medicine offers a promising framework for achieving personalized phenotypes.

Conclusions:

• Effective Personalized Medicine necessitates a holistic view beyond single genetic factors.
• Systems Medicine, augmented by AI, is crucial for developing advanced clinical phenotyping strategies.
• Integrating diverse data sources is key to realizing the full potential of Personalized Medicine for complex disorders.