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Fast-and-frugal decision trees for clinicians
Konstantinos V Katsikopoulos1, Susana Pereira2, Niklas Keller3
1Department of Decision Analytics and Risk, Southampton Business School, University of Southampton, Southampton, UK.
Presse Medicale (Paris, France : 1983)
|June 24, 2026
Summary
Fast-and-frugal trees offer intuitive, simplified decision-making models for clinicians, overcoming the complexity of standard models. These heuristics use minimal information for effective clinical support, enhancing accuracy and transparency in artificial intelligence applications.
Area of Science:
- Decision Science
- Medical Informatics
- Artificial Intelligence
Background:
- Standard decision models face clinical resistance due to complexity and inability to handle medical data's ill-defined nature.
- Fast-and-frugal heuristics provide intuitive decision-making tools using limited information and simple logic.
Purpose of the Study:
- To define, discuss, and illustrate the construction of fast-and-frugal trees for clinical decision support.
- To explore applications of fast-and-frugal trees in fetal monitoring and intensive care unit assignment.
- To discuss future directions for theory and applications, particularly concerning AI in clinical decision-making.
Main Methods:
- Review of literature on fast-and-frugal heuristics and trees.
- Illustrative examples of building and applying fast-and-frugal trees.
- Discussion of case studies in fetal monitoring and ICU assignment.
Main Results:
- Fast-and-frugal trees are a successful family of heuristics applicable to clinical practice.
- Demonstrated utility in supporting critical decisions in fetal monitoring and ICU allocation.
- Identified challenges and opportunities for integrating these heuristics with AI.
Conclusions:
- Fast-and-frugal trees offer a practical alternative to complex decision models in medicine.
- These heuristics can enhance the accuracy and transparency of AI-driven clinical decision support systems.
- Further research is needed to bridge theory and application for robust AI in healthcare.
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