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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.
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