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sbornikkonferenczii19iyunya2023
| Introduction.
In the context of clinical practice, ML algorithms can be trained on large volumes of patient data, such as electronic health records, medical imaging scans, genetic information, and clinical notes. By learning from these datasets, ML models can assist healthcare professionals in various tasks, including disease diagnosis, risk prediction, treatment planning, and patient monitoring. ML algorithms can detect subtle patterns and associations within medical data that may not be easily recognizable by humans. They can identify key features and markers indicative of specific conditions or treatment outcomes. This enables healthcare providers to make more accurate and personalized decisions, leading to improved patient outcomes and efficient resource utilization. Moreover, ML models can continuously learn and adapt from new data, allowing for iterative improvements and real-time updates to clinical decision support systems. This iterative process enhances the precision and reliability of predictions over time, enabling healthcare professionals to stay updated with the latest evidence-based practices. While ML has great potential in clinical practice, it is important to ensure proper validation, transparency, and ethical considerations. Rigorous evaluation and validation of ML algorithms are crucial to ensure their reliability and safety before their integration into healthcare workflows. Additionally, maintaining patient privacy, data security, and maintaining a human-centric approach in decision- making remain essential aspects of ML adoption in clinical settings. In summary, ML in clinical practice harnesses the power of data and algorithms to augment healthcare professionals' decision-making processes (Fig. 1) . By analyzing medical data, ML algorithms can provide valuable insights, improve diagnostic accuracy, and enhance patient care, ultimately leading to better health outcomes. |