  This study analyzes hybrid AI systems' design patterns and their
effectiveness in clinical decision-making using the boxology framework. It
categorizes and copares various architectures combining machine learning and
rule-based reasoning to provide insights into their structural foundations and
healthcare applications. Addressing two main questions, how to categorize these
systems againts established design patterns and how to extract insights through
comparative analysis, the study uses design patterns from software engineering
to understand and optimize healthcare AI systems. Boxology helps identify
commonalities and create reusable solutions, enhancing these systems'
scalability, reliability, and performance. Five primary architectures are
examined: REML, MLRB, RBML, RMLT, and PERML. Each has unique strengths and
weaknesses, highlighting the need for tailored approaches in clinical tasks.
REML excels in high-accuracy prediction for datasets with limited data; MLRB in
handling large datasets and complex data integration; RBML in explainability
and trustworthiness; RMLT in managing high-dimensional data; and PERML, though
limited in analysis, shows promise in urgent care scenarios. The study
introduces four new patterns, creates five abstract categorization patterns,
and refines those five further to specific systems. These contributions enhance
Boxlogy's taxonomical organization and offer novel approaches to integrating
expert knowledge with machine learning. Boxology's structured, modular apporach
offers significant advantages in developing and analyzing hybrid AI systems,
revealing commonalities, and promoting reusable solutions. In conclusion, this
study underscores hybrid AI systems' crucial role in advancing healthcare and
Boxology's potential to drive further innovation in AI integration, ultimately
improving clinical decision support and patient outcomes.
