Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift

Area of Science:

• Complex Systems Mathematics
• Computer Science
• Medical Informatics

Background:

• Individual patient characteristics differ from population averages.
• Medical interventions effective for populations may not suit individuals.
• Current medical guidelines may not align with patient preferences or yield favorable outcomes.

Purpose of the Study:

• To reconfigure medical thought using non-linear dynamics and chaos theory.
• To introduce Artificial Adaptive Systems for complex medical problem-solving.
• To support individualized patient care and overcome translational failures.

Main Methods:

• Utilizing Artificial Neural Networks (ANNs) and Evolutionary Algorithms (EA) from complex systems mathematics.
• Applying computer science to create intelligent agents capable of dynamic adaptation.
• Incorporating Fuzzy Logic for nuanced decision-making, acknowledging the spectrum between health and disease.

Main Results:

• ANNs and EA can model complex interactions of multiple factors simultaneously.
• These systems aid clinicians in decision-making under uncertainty for individual cases.
• Facilitates efficient technology transfer from medical science to practice, addressing translational gaps.

Conclusions:

• Artificial Adaptive Systems offer a holistic approach to patient management, contrasting statistical reductionism.
• Enables medical decisions based on individual patient data, not just average trends.
• Promotes a new humanism in medicine focused on the unique patient subject.