Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review

Background: As the application of Artificial Intelligence (AI) technologies increases in the healthcare sector, the industry faces a need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML), which offer high prediction accuracy at the expense of transparency of decision making.Purpose: This paper seeks to review the present literature, identify hybrid architecture patterns that incorporate rules and machine learning, and evaluate the rationale behind their selection to inform future development and research on the design of transparent and precise clinical decision systems.Methods: PubMed, IEEE Explore, and Google Scholar were queried in search for papers from 1992 to 2022, with the keywords: "clinical decision system", "hybrid clinical architecture", "machine learning and clinical rules". Excluded articles did not use both ML and rules or did not provide any explanation of employed architecture. A proposed taxonomy was used to organize the results, analyze them, and depict them in graphical and tabular form. Two researchers, one with expertise in rule-based systems and another in ML, reviewed identified papers and discussed the work to minimize bias, and the third one re-reviewed the work to ensure consistency of reporting.Results: The authors screened 957 papers and reviewed 71 that met their criteria. Five distinct architecture ar-chetypes were determined: Rules are Embedded in ML architecture (REML) (most used), ML pre-processes input data for Rule-Based inference (MLRB), Rule-Based method pre-processes input data for ML prediction (RBML), Rules influence ML training (RMLT), Parallel Ensemble of Rules and ML (PERML), which was rarely observed in clinical contexts.Conclusions: Most architectures in the reviewed literature prioritize prediction accuracy over explainability and trustworthiness, which has led to more complex embedded approaches. Alternatively, parallel (PERML) archi-tectures may be employed, allowing for a more transparent system that is easier to explain to patients and cli-nicians. The potential of this approach warrants further research.Other: A limitation of the study may be that it reviews scientific literature, while algorithms implemented in clinical practice may present different distributions of motivations and implementations of hybrid architectures.