A AI-ASSISTED CLINICAL DECISION SUPPORT SYSTEMS: ENHANCING DIAGNOSTIC ACCURACY AND TREATMENT RECOMMENDATIONS

Abstract

The purpose of this paper is to establish the positive developments of AI-assisted Clinical Decision Support Systems (CDSS) in deciphering diagnostic precision and therapeutic advice in the healthcare sector. This literature review starts from the definition of AI in the context of healthcare before dissecting the development of AI technologies and the way they have been assimilated into practical clinical use. This paper evaluates AI-based CDSS with special emphasis on machine learning algorithms and the integration with Electronic Health Records electronic records. In this respect, having presented an understanding of case studies and involving comparative assessment, the review illustrates how AI enhances the efficiency of diagnosis and better than conventional practice. The role of AI in the healthcare sector is omnipresent and intelligible. The capability of modern developed AI techniques might contribute substantially to both therapeutic support (diagnosis and treatment approach) as well as public healthcare policies [1]. AI-assisted clinical decision support tools can offer clinicians access to information derived from thousands of similar and real patients and can significantly influence diagnostic accuracy and improve treatment decisions. Moreover, it goes over the customized plan of treatment to be made by AI and its contribution to the precision of medicine along with the right way in the treatment path. The review includes pros of using AI-supporting CDSS including increased productivity and better outcomes of the therapy; the review focuses on some crucial questions related to the ethical issues, legislation, and trust in AI-supporting CDSS. Thus, the review offered in this paper aims at shedding light on the state of AI in CDS nowadays and pointing to its possible directions that may facilitate future developments in the scope [1]. Also, the review addresses the legal and/or moral implications that come with the use of the AI-supported CDSS. Instead, it discusses the legal and regulatory measures of AI solutions to healthcare and emphasizes on the proper practices. These include issues in the algorithm selection by AI and protection of data in order to realize appropriate integration of the AI technologies [2]. Also, the paper focuses on the enhancement of the compatibility of AI with existing frameworks like EHR to enhance data sharing. In this way, the review yields ideas about the barriers and potentials that concerns the ethical and regulative aspects of AI-supported CDSS application. This integration assures proper patient care and satisfaction of the patients by providing better treatment plans for their conditions. Apart from the above-mentioned specific contributions of the AI-assisted CDSS, it has a number of pre-stipulated values that could benefit in the future. More to this, the systems in use will progress to greater heights especially in the analysis, diagnosis, and even prediction of future events. Further innovative studies and experiences point to a promising breakthrough in the field of tailored medicine as well as the optimisation of costs [2,3].