Using constrained-disorder principle-based systems to improve the performance of digital twins in biological systems (Preprint)

Abstract

UNSTRUCTURED Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve outputs; however, biological systems are characterized by inherent variability, which is mandatory for their proper function. In the present paper, we review the role of noise in complex systems and its use in bioengineering. Review of recent literature We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing signatures of variabilities while simultaneously reducing the amount of unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is mandatory for the future design of improved, more accurate digital twins.

Department of Medicine 2040: Implementing a Constrained Disorder Principle-Based Second-Generation Artificial Intelligence System for Improved Patient Outcomes in the Department of Internal Medicine

“…[1][2][3] CDP-based enhanced digital twins are presented to improve diagnosis accuracy and personalized treatments and follow-ups. 4…”