Enhancing trustworthiness in model-guided medicine with a model identity certificate (MIC): starting with...

Model-guided medicine (MGM) represents a paradigm shift in clinical practice, emphasizing the integration of computational models to support diagnosis, therapy planning and individualized patient care. The general and/or specific domain models, on which recommendations, decisions or actions of these systems are based, should reflect in their model identity certificate (MIC) the level of model relevance, truthfulness and transparency.

Methods and tools for building models and their corresponding templates for a MIC in the domains of radiology and surgery should be drawn from relevant elements of a model science, specifically from mathematical modelling methods (e.g. for model truthfulness) and modelling informatics tools (e.g. for model transparency). Other elements or MIC classes to consider may include ethics, human-AI model interaction and model control.

A generic template of a MIC with classes, attributes and examples for the general domain of health care is being proposed as an initial attempt to gain experience with the complexity of the problems associated with enhancing trustworthiness in models. This template is intended to serve as a framework for an instance of a specific template for robot assisted intervention for hepatocellular cancer within the domain of interventional radiology (work-in-progress).

Gaining trustworthiness in intelligent systems based on models and related AI tools is a challenging undertaking and raises many critical questions, specifically those related to ascertain model relevance, truthfulness and transparency. The healthcare system, in particular the interventional medical disciplines, will have to be concerned about the availability of digital identity certificates to enable the control for these systems and related artefacts, e.g. digital twins, avatars, diagnostic and interventional robots, or intelligent agents.