<b>Artificial Intelligence-based Clinical Decision Support Systems: Enhancing Modern Healthcare for Smart Diagnosis and Prognosis</b>

Abstract

The advancement of Artificial Intelligence (AI) is enhancing healthcare by enabling accurate, efficient, and personalized clinical decision-making. Moreover, a smart diagnosis (CAD) system combining machine learning, natural language processing, and data analytics to assist doctors in finding the right recommendations is key. These devices process a large volume of both structured and unstructured data, such as computerized medical records, medical imaging, and genomics, to derive evidence-based recommendations and predictive insights. Hence, combining various data sources with AI systems accelerates the diagnostic process and enables the detection of intricate patterns that may elude human doctors, resulting in fast and more reliable medical decisions. This paper introduces the structure, capabilities, and role in clinical practice of AI and radiology. The fusion of the algorithms with cutting-edge imaging techniques enhances the early detection of diseases, supports tailored treatment options, and enables precise treatments. In particular, AI-Clinical Decision Support Systems allow for accurate analysis of images and minimize differences in interpretations between radiologists, a problem that has long existed in the field of radiology. Additionally, it also emphasizes their role in improving diagnoses and patient safety. Notably, AI-powered clinical decision support systems (AI-CDSS) have significant potential to reduce errors in diagnosis, as evidenced in numerous clinical contexts. Equally important, their flexibility in high-resource clinical environments as well as low-resource environments emphasizes their universal relevance and innovative potential. Beyond these benefits, this review further addresses the ethical, legal, and practical issues that these challenges raise, such as equal access, automatic discrimination, and medical responsibility. Moreover, other vital key points taken into consideration include protecting patient privacy, making sure AI decision-making processes are clear and understandable, and implementing policies that encourage innovation while maintaining integrity. Hence, leveraging AI-based clinical decision support systems (AI-CDSS) responsibly and effectively can transform healthcare, thereby leading to greater diagnostic accuracy and improved patient-specific outcomes.

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