Artificial-intelligence-driven Innovations in Mechanistic Computational Modeling and Digital Twins for Biomedical...

Area of Science:

• Computational Biology
• Biomedical Informatics
• Systems Biology

Background:

• Complex biological systems present challenges in understanding due to high dimensionality and nonlinearity.
• Mechanistic modeling offers interpretability but struggles with scalability and parameter estimation.
• Artificial intelligence (AI) excels at integrating multi-omics data for predictions but lacks interpretability.

Purpose of the Study:

• To review recent advancements in integrating AI and mechanistic modeling for biomedical applications.
• To address the limitations of individual AI and mechanistic modeling approaches.
• To highlight the potential of integrated models for scientific discovery.

Main Methods:

• Review of recent literature on AI and mechanistic modeling integration.
• Focus on advancements in computational models and AI algorithms.
• Exploration of applications in biology, pharmacology, drug discovery, and disease modeling.

Main Results:

• AI aids mechanistic modeling by enabling new discoveries and providing insights into AI predictions.
• Integration facilitates the modeling of complex systems and estimation of experimental parameters.
• Surrogate models are developed to reduce computational costs associated with mechanistic simulations.

Conclusions:

• The integration of AI and mechanistic models is crucial for advancing biological discoveries and understanding disease mechanisms.
• This synergy supports drug development and personalized medicine.
• Advanced biomedical models, including medical digital twins and virtual patients, benefit from this integration.