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Making "CASES" for AI in Medicine.

Ge Wang¹

¹Biomedical Imaging Center, Center for Computational Innovations, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.

|January 30, 2024

Summary

This perspective introduces CASES, a framework for evaluating AI in medicine, encompassing Confidence, Adaptability, Stability, Explainability, and Security. These factors can be synergistically addressed using large model platforms and diffusion-type models.

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Area of Science:

Artificial Intelligence in Medicine
Medical Informatics
AI Ethics

Background:

The integration of Artificial Intelligence (AI) into medicine necessitates robust evaluation frameworks.
Existing AI systems may face challenges in meeting critical performance and safety standards.
A structured approach is needed to assess the multifaceted nature of AI in healthcare.

Purpose of the Study:

To propose a novel framework, CASES, for evaluating AI systems in medicine.
To define the core components of the CASES framework: Confidence, Adaptability, Stability, Explainability, and Security.
To highlight the synergistic potential of addressing these components within advanced AI platforms.

Main Methods:

Conceptual framework development based on expert perspective.
Identification of key attributes for AI system evaluation in a medical context.
Exploration of synergistic approaches using large model platforms and diffusion-type models.

Main Results:

The CASES framework provides a comprehensive approach to AI evaluation in medicine.
Individual components of CASES (Confidence, Adaptability, Stability, Explainability, Security) are critical.
Synergistic application of CASES on large model platforms offers enhanced AI system performance and safety.

Conclusions:

The CASES framework offers a vital perspective for the responsible development and deployment of AI in medicine.
Addressing Confidence, Adaptability, Stability, Explainability, and Security collectively is crucial for trustworthy AI.
Advanced AI architectures, including large models and diffusion-type models, can effectively implement the synergistic CASES approach.