From engineering principles to healthcare practice: A hybrid reasoning framework for transparent clinical decision support

Clinical decision support systems (CDSS) are increasingly reliant on purely data-driven machine learning models, leading to significant challenges in clinical adoption due to their “black-box” nature, high risk of algorithmic bias, and inability to enforce hard safety constraints. This lack of transparency and clinical alignment poses major challenges for regulatory compliance and professional trust. This study proposes a novel hybrid artificial intelligence (AI) meta-model for CDSS design, which formally translates established engineering decision support paradigms into the clinical domain to create systems that are inherently safer and more explainable. The framework rigorously integrates: (i) Web Ontology Language 2 for formalizing medical concepts; (ii) a semantic web rule language rule base to serve as “clinical guardrails” for enforcing evidence-based guidelines and safety constraints; and (iii) a modular inference policy that intelligently matches decision problems with specific data-driven or probabilistic methods. A prototype was implemented, combining this explicit knowledge layer with modular inference engines and template-based explanations. Evaluation across two large-scale clinical tasks (oncology using the Surveillance, Epidemiology, and End Results database and intensive care using the Medical Information Mart for Intensive Care-IV database) demonstrated superior performance over data-driven baselines. Specifically, the hybrid system achieved a 78% reduction in guideline-violation errors (reducing the contraindication rate from 18% to 6%) while maintaining high predictive accuracy (area under the receiver operating characteristic curve of 0.84 and 0.87, respectively). Furthermore, a clinician usability study confirmed that the transparent, knowledge-driven explanations resulted in significantly higher decision clarity (Likert rating of 6.2/7) and reduced cognitive load. The findings validate that this hybrid AI architecture represents a robust and transferable approach to designing clinically aligned, trustworthy, and explainable CDSS, directly addressing critical requirements for the responsible deployment of AI in healthcare.