Raqamli texnologiyalarning Yangi O‘zbekiston rivojiga ta’siri

 
RAQAMLI TEXNOLOGIYALARNING 
YANGI 
O‘ZBEKISTON
 RIVOJIGA 
TA’SIRI
 
Xalqaro ilmiy-amaliy konferensiyasi
 
3.
 
AI-Enabled Diagnostic Systems 
3.1.
 
AI in Clinical Decision Support 
AI in clinical decision support (CDS) systems has the potential to significantly enhance the 
diagnosis, treatment, and prognosis of various medical conditions. These systems utilize artificial 
intelligence algorithms to analyze biomedical imaging data and predict the probability of a medical 
outcome or the risk of a specific disease. By leveraging the power of AI, CDS systems can assist 
clinicians in collecting, understanding, and making inferences from vast amounts of patient data, 
ultimately leading to optimal clinical decision-making. 
The integration of AI in clinical decision support holds immense promise for improving patient 
care and outcomes. AI algorithms can process and analyze complex datasets, including genomic 
information, biomarkers, phenotypic data, electronic health records, and care delivery data, to 
provide clinicians with valuable insights and predictions. By leveraging these AI-enabled systems, 
clinicians can make more informed decisions, personalize treatment plans, and optimize patient 
management. 
However, the implementation of AI in clinical decision support comes with several challenges. 
One key consideration is the design, development, selection, use, and ongoing surveillance of AI 
systems. Evaluating the safety and effectiveness of AI-enabled CDS systems is crucial, especially 
given their dynamic nature and the utilization of vast amounts of diverse data. Robust evaluation 
frameworks and methodologies are necessary to assess the performance, reliability, and 
generalizability of these systems in real-world clinical settings. 
Furthermore, the integration of AI in CDS raises questions regarding ethical and legal 
considerations. Privacy, security, and data governance become essential aspects when dealing with 
sensitive patient information and ensuring compliance with relevant regulations. Transparency and 
interpretability of AI algorithms are also important for clinicians to understand the reasoning 
behind the system's recommendations and build trust in its capabilities. 
Moreover, the implementation and adoption of AI in clinical decision support require effective 
collaboration between healthcare professionals, data scientists, and developers. Integration with 
existing clinical workflows, electronic health record systems, and interoperability with other 
healthcare technologies are critical for seamless integration and successful utilization of AI-based 
CDS systems.