Artificial Intelligence-Driven Clinical Decision Support Systems: Enhancing Diagnostic Accuracy and Patient Outcomes
Keywords:
artificial-intelligence, clinical-decision-support, diagnostic-accuracy, machine-learning, healthcare-innovation, precision-medicineAbstract
Artificial intelligence-powered clinical decision support systems (AI-CDSS) represent a transformative paradigm in modern medical diagnosis, yet evidence on their implementation and clinical impact remains fragmented. This prospective study evaluated AI-CDSS effectiveness across five specialties at FMIOPH "VIVARY" between January 2024–December 2025, analyzing 12,847 diagnostic cases. AI systems achieved superior accuracy versus clinician-alone diagnoses: radiology lung nodule detection (94% vs. 65%, p<0.001), breast cancer classification (90% vs. 78%, p<0.001), ECG arrhythmia diagnosis (84% vs. 71%, p=0.002). False positive rates decreased 31% with AI assistance; diagnostic time reduced by 42%. Integration of interpretability modules increased physician trust from 52% to 89% (p<0.001). However, off-target recommendations occurred in 8% of complex cases, necessitating human oversight. This study establishes AI-CDSS as clinically viable adjuncts enhancing diagnostic precision without replacing clinical judgment, with particular benefit in resource-constrained settings.
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