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Area of Science:

  • Biophysics
  • Computational Biology
  • Medical Informatics

Background:

  • Diagnostic errors are a major healthcare challenge, causing millions of adverse outcomes annually.
  • Current diagnostic methods, relying on heuristics and black-box AI, are limited by the human body's nonlinear complexity.
  • Physiology is fundamentally a multiscale, adaptive, nonlinear dynamical system, with nonlinear phenomena crucial in health and disease.

Purpose of the Study:

  • To highlight the essential role of nonlinear mechanics and dynamics in understanding human physiology and improving medical diagnostics.
  • To propose a synthesis of mechanistic models, artificial intelligence (AI), and clinical expertise for enhanced diagnostic accuracy.
  • To advocate for physics-informed machine learning and digital twins for personalized and interpretable diagnostics.

Main Methods:

  • Reviewing the prevalence and impact of nonlinear phenomena across physiological systems (cardiovascular, neural, etc.).
  • Analyzing the limitations of isolated approaches: mechanistic models, data-driven AI, and physician judgment.
  • Proposing hybrid frameworks integrating mechanistic insights, data-driven AI, and clinical wisdom.

Main Results:

  • Nonlinear phenomena are integral to all biological systems and central to health and disease.
  • Isolated diagnostic approaches are insufficient; a synthesis is required for mechanistic understanding.
  • Physics-informed machine learning and digital twins offer a framework for combining diverse strengths.

Conclusions:

  • Ignoring nonlinear dynamics hinders accurate diagnostics and personalized medicine.
  • Integrating nonlinear mechanics into patient-specific hybrid models is key to reducing diagnostic errors.
  • This approach facilitates a transition to proactive, precision medicine from reactive, guideline-driven care.