Author(s): Maria Torres and Ahmed Al-Mansoori

Abstract: Case-Based Reasoning (CBR) - an advanced technique is a problem-solving paradigm that utilizes past experiences to solve new problems by finding similar cases. In healthcare, CBR has gained prominence as a tool for diagnosis, treatment planning, and medical decision support. The healthcare sector, characterized by its complexity and variability, benefits significantly from CBR due to its ability to provide personalized solutions based on historical patient data. This paper explores the applications of CBR in healthcare, focusing on areas such as medical diagnosis, prognosis prediction, treatment customization, and clinical decision support. Additionally, it examines the challenges associated with implementing CBR systems, including data quality, case representation, knowledge retrieval, and system integration. One of the main obstacles in the widespread adoption of CBR in healthcare is the heterogeneity of medical data, which complicates the process of identifying similar cases. Furthermore, the need for large, high-quality datasets and the development of effective case retrieval algorithms are crucial for improving the efficacy of CBR systems. Despite these challenges, CBR holds significant potential for enhancing clinical decision-making and improving patient outcomes. The paper concludes by discussing the future directions of CBR in healthcare, particularly the integration of machine learning techniques to enhance case retrieval and decision-making processes. As healthcare continues to evolve, CBR is poised to play an increasingly critical role in supporting evidence-based practices and personalized patient care.

DOI: 10.33545/26633582.2026.v8.i1a.242

Pages: 14-18 | Views: 74 | Downloads: 39

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