Abstract
Technology is becoming vital in improving diagnostic accuracy in modern medical field. The diagnostic process, like a production chain, consists of databases (resourc-es), the diagnostic activities (production), and the ultimate diagnosis (outcome). However, the intricacy of medical diagnostics has evolved due to the swift proliferation of medical knowledge and immense amount of patient data. Physicians encounter the complexity of handling huge volumes of clinical data,. To address these issues, the work examines the use of automated diagnostic systems supported by sophisticated mathematical models. The aim of the project is to investigate novel mathematical methods for disease detection and predic-tion, with a specific emphasis on fuzzy set theory. Particularly when dealing with imprecise or confusing initial data, such as medical histories and laboratory results, these techniques are indispensable. The authors investigate the function of expert systems and machine learning in the diagnosis of diseases such as gallstone formation and the detection of the diseases like sclerosis using medical imaging algorithms. These systems are specifically developed to as-sist medical professionals in making sound judgments, thereby presenting a very promising prospect for medical diagnostics through the integration of knowledge from several academic fields. The use of such technology will enhance clinical procedures, optimizing both the pre-cision and effectiveness of diagnoses while increasing healthcare accessibility .