Raqamli texnologiyalarning Yangi O‘zbekiston rivojiga ta’siri

 
RAQAMLI TEXNOLOGIYALARNING 
YANGI 
O‘ZBEKISTON
 RIVOJIGA 
TA’SIRI
 
Xalqaro ilmiy-amaliy konferensiyasi
 
facilitate seamless interaction between CAD systems and medical professionals. Overcoming these 
technical and logistical hurdles will be essential for the successful integration and utilization of CAD 
technology in healthcare settings. 
2.3.
 
AI in Medical Imaging Research 
In the field of medical imaging research, artificial intelligence (AI) has emerged as a powerful 
tool for addressing various challenges, including image registration, reconstruction, classification, 
detection, segmentation, diagnosis, and prognosis. AI algorithms have been developed and applied 
to handle these issues, aiming to improve the accuracy and efficiency of medical image analysis. 
The application of AI in medical imaging research has enabled automated disease detection, 
characterization of histology, staging, subtype identification, and patient classification based on 
therapy outcomes or prognosis. By analyzing medical images with AI algorithms, researchers can 
extract valuable information and patterns that might not be readily discernible to the human eye. 
These AI tools provide imaging professionals with decision support systems that offer actionable 
advice, ultimately improving patient outcomes. 
One of the primary objectives of AI research in medical imaging is to create robust and reliable 
tools that enhance the accuracy and efficiency of clinical practice. However, several challenges need 
to be addressed in the utilization of AI in medical imaging research. 
Firstly, current approaches in AI, particularly deep learning, are known to be data-hungry. 
Robust AI algorithms require large amounts of high-quality medical data and metadata for training. 
Gathering such datasets can be challenging, especially when considering privacy regulations and 
the need for diverse and representative samples. Overcoming these data challenges is crucial for 
developing AI algorithms that can generalize well across different patient populations and imaging 
modalities. 
Another challenge in AI-based medical imaging research is the robustness of algorithms. AI 
models are highly sensitive to variations in input data, including changes in image acquisition 
protocols, image quality, and patient demographics. Ensuring the robustness and generalizability of 
AI algorithms is critical to their successful deployment in clinical settings. 
Furthermore, medical imaging informatics plays a significant role in AI research by improving 
the efficiency, accuracy, and reliability of medical image usage and exchange within complex 
healthcare systems. Informatics approaches facilitate the integration and interoperability of AI 
algorithms with existing imaging infrastructures and electronic health record systems. By enabling 
seamless data sharing and integration, medical imaging informatics supports the development and 
translation of AI tools into clinical practice. 

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