Raqamli texnologiyalarning Yangi O‘zbekiston rivojiga ta’siri

2.
 
AI in Medical Imaging 
2.1.
 
Radiology and AI 
Radiology, as a specialized medical field, heavily relies on medical imaging techniques for the 
detection and diagnosis of diseases. The integration of Artificial Intelligence (AI) in radiology has 
the potential to revolutionize the practice by augmenting the capabilities of radiologists and 
improving the accuracy and efficiency of image interpretation. 
The use of AI in diagnostic medical imaging is undergoing extensive evaluation. AI has shown 
impressive accuracy and sensitivity in the identification of imaging abnormalities and promises to 

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168 
 
RAQAMLI TEXNOLOGIYALARNING 
YANGI 
O‘ZBEKISTON
 RIVOJIGA 
TA’SIRI
 
Xalqaro ilmiy-amaliy konferensiyasi
 
enhance tissue-based detection and characterization. By leveraging the massive computing abilities 
of machine learning, AI is evolving medical imaging by mining body scans for valuable insights. 
AI is poised to broadly reshape medicine, potentially improving the experiences of both 
clinicians and patients. Prospective studies and advances in medical image analysis have reduced 
the gap between research and deployment, bringing AI closer to practical applications in the field 
of radiology. 
One of the most significant advancements in AI in radiology is its potential to help with cancer 
detection. Deep neural networks have been trained to automatically analyze radiology images and 
digitized pathology slides for numerous different cancer types. For example, deep learning can be 
used to detect mammographic lesions with an accuracy that rivals that of certified screening 
radiologists. This application of AI in cancer diagnosis has the potential to improve early detection 
rates and increase the efficiency of screening programs. 
Furthermore, AI is being used to predict responders to certain cancer therapies, such as 
immune therapies or chemotherapies, whose biological determinants of response are thought to be 
multifactorial. By analyzing various clinical and molecular data, AI models can identify patients who 
are more likely to benefit from specific treatments, enabling a more personalized and targeted 
approach to cancer therapy. 
In addition to cancer detection, AI applications in radiology are expanding to include new 
approaches for cancer screening, diagnosis, and classification. AI algorithms are being developed to 
analyze tumor genomics, assess the tumor microenvironment, identify prognostic and predictive 
biomarkers, and even aid in drug discovery efforts. 
The integration of AI in radiology holds great promise for improving diagnostic accuracy, 
workflow efficiency, and patient outcomes. With its ability to analyze vast amounts of imaging data, 
identify subtle abnormalities, and provide decision support, AI has the potential to significantly 
enhance the capabilities of radiologists and improve the overall quality of healthcare in cancer 
detection and treatment.