Morbi et tellus imperdiet, aliquam nulla sed, dapibus erat. Aenean dapibus sem non purus venenatis vulputate. Donec accumsan eleifend blandit. Nullam auctor ligula

Get In Touch

Quick Email
[This article belongs to Volume - 26, Issue - 04]


Artificial Intelligence (AI) technologies integrated into health care is due to the growing digitalization of society. To ensure appropriate digitalization in the clinical sector, it is crucial to assess and examine the potential diagnosis and treatment for disease at an earlier level of advancement. The integration of AI into healthcare holds significant promise and is gradually making strides in medical practice. Clinical Decision Support Systems (CDSS) play a crucial role in assisting healthcare professionals with their decision-making processes. The collaboration between medical professionals and AI specialists becomes paramount for the effective implementation of these systems. This article aims to provide an overview of AI in healthcare, particularly centering on Clinical Decision Support Systems (CDSS). The primary objective is to investigate the epistemic concerns that arise during the creation of CDSS, placing emphasis on alignment with technology and fostering collaboration between medical practitioners and AI specialists. Concentrating specifically on CDSS, this review examines the epistemic challenges inherent in their creation and execution. A search was conducted on PubMed, Scopus, and Google Scholar using keywords such as AI, CDSS, Algorithms, Deep learning, Machine learning, and diagnosis. The review consolidates information regarding the cognitive responsibilities of medical practitioners, emphasizing their involvement in constructing comprehensive patient profiles. This paper scrutinizes the primary challenges associated with evaluating clinical decision support systems enabled by AI throughout their stages of design, development, selection, use, and ongoing surveillance. The practical aspects of evaluating AI in healthcare are explored, encompassing discussion on evaluation approaches and identification of indicators for monitoring AI performance. The review discusses the importance of epistemic tasks, highlighting AI in CDSS's role in statistical reasoning and pattern recognition extending to clinicians' responsibilities in constructing patient profiles and making decisions based on epistemological considerations. The role of experts in CDSS development is explored, with a specific focus on creating systems that are both explainable and accountable in interactions with clinicians, while also establishing direct connections between CDSS and individual patients. Medical professionals need to employ AI in CDSS epistemologically, which may help and even enhance clinical decision-making abilities.