AI-Driven Clinical Decision Support Systems: Transforming Diagnostic Accuracy In Modern Healthcare
DOI:
https://doi.org/10.29284/2sz49210Keywords:
Artificial intelligence; clinical decision support systems; diagnostic accuracy; machine learning; deep learning; electronic health records; healthcare informatics.Abstract
Background: Clinical Decision Support Systems (CDSS) have been transformed by Artificial Intelligence (AI) in many medical fields, affecting clinical practice. This study aims to systematically assess the effect of AI-CDSS on diagnostic accuracy, clinical efficiency and patient outcomes compared to the traditional clinician-only decision-making process.
Methods: A mixed-methods analysis was performed which included a systematic review of 84 published, peer-reviewed studies between 2020-2025 followed by a retrospective cohort analysis of 218,000 de-identified patient records from 7 tertiary-care institutions. The following performance measures were collected and collated: sensitivity, specificity, Area Under the Receiver Operating Characteristic (AUC-ROC) curve, and time-to-diagnosis.
Findings: The diagnostic accuracy of AI-CDSS was statistically superior to that of the clinicians (AUC-ROC: 0.952 vs 0.901; p < 0.001). The results showed that the hybrid AI-clinician model outperformed the others on all the metrics, with a 78% drop in diagnostic error rate and 92% drop in mean time-to-diagnosis. The largest improvements in accuracy were seen in radiology (94.7%), pathology (96.2%) and ophthalmology (97.5%).
Conclusions: AI-CDSS is a clinically important step forward in the field of diagnostics. The potential for full translation will depend on the concurrent implementation of data governance, algorithmic fairness and regulatory alignment, in addition to the optimal deployment of a collaborative AI-human framework.
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