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sbornikkonferenczii19iyunya2023

Conclusions 
In conclusion, Machine Learning (ML) has emerged as a 
powerful tool in clinical decision-making, offering numerous benefits to healthcare 
providers, patients, and healthcare systems. By analyzing large volumes of patient 
data, ML algorithms can provide valuable insights, improve diagnostic accuracy, 
and enhance patient care. ML algorithms can detect subtle patterns and associations 
within medical data, leading to more accurate and personalized decisions and 
improved patient outcomes. Additionally, ML in Clinical Decision Support Systems 
(CDSS) empowers healthcare professionals with real-time recommendations and 
predictive models, enhancing their decision-making capabilities. Patients can benefit 
from ML-based CDSS by receiving personalized guidance and treatment options, 
leading to increased engagement and better health outcomes. Healthcare systems can 
optimize resource allocation, improve efficiency, and strategically plan 
interventions by leveraging ML's capabilities. The future prospects of ML in 
healthcare are promising, with precision medicine, predictive analytics, healthcare 
resource optimization, and enhanced CDSS being key areas of focus. As ML 
continues to advance and integrate with other technologies, it has the potential to 
revolutionize healthcare by enabling personalized medicine, early disease detection, 
resource optimization, and enhanced decision support systems. It is crucial to ensure 
rigorous validation, ethical considerations, and a human-centric approach in the 
integration of ML into clinical practice to maximize its benefits and ensure patient 
safety. Overall, ML in clinical decision-making holds great promise in transforming 
healthcare and improving patient outcomes. 
 
УДК: 616 
МРНТИ: 76 
 
CONDUCTING A COMPARATIVE ANALYSIS OF THE NATURE AND 


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