GEMOLITIK VA MEXANIK SARIQLIKLARNI KLINIK VA LABORATOR MEZONLAR ASOSIDA DIFFERENSIAL TASHXISLASHDA SUN'IY INTELLEKT TEXNOLOGIYALARINING QO'LLANILISHI VA DIAGNOSTIK SAMARADORLIGI

Hemolytic and obstructive jaundice present with similar clinical manifestations but differ significantly in etiology and treatment strategies. This study evaluates the effectiveness of artificial intelligence (AI) technologies in differential diagnosis based on clinical and laboratory parameters. Analysis of data from 200 patients demonstrated that machine learning models incorporating bilirubin levels and fractions, lactate dehydrogenase (LDH), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), and clinical features achieve high diagnostic accuracy (AUC = 0.94). The application of AI reduces diagnostic errors and improves clinical decision-making processes.