ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING: ENHANCING DIAGNOSTIC ACCURACY

Abstract

The healthcare industry underwent revolutionary changes because medical imaging systems enable early disease identification combined with precise diagnoses while planning successful treatments. The development of MRI, CT scanning, and ultrasound technologies boosted diagnostic accuracy throughout multiple years. The assessment process that uses traditional imaging methods depends on human readers, who may produce inconsistent results. This research evaluates how contemporary imaging systems improve medical diagnosis for cancer patients and cardiovascular and neurological disease patients. The research demonstrates better testing precision through improved sensitivity and specificity measures decreased occurrences of wrong positive and negative readings and enhanced operational workflow processes. The implementation of modern imaging technologies provides advantages but encounters essential obstacles which consist of privacy concerns as well as regulatory obligations and expensive implementation requirements. Researchers are currently working on healthcare imaging technique optimization and transparency enhancement while trying to establish wider healthcare access through future medical advancements. Research along with policy reforms need to tackle existing challenges because they will ensure medical imaging reaches its full potential to increase patient care quality and clinical success rates.