The Impact of Artificial Intelligence on Patient Care and Clinical Outcomes
Keywords:
Artificial Intelligence, Clinical Decision Support Systems, Patient Outcomes, Medical Imaging, Predictive ModelingAbstract
CDSS has evolved and AI technology has become an essential component in healthcare because it increases efficiency in patient management and care. This study analyzes the effectiveness of four AI algorithms, namely logistic regression, CNNs, random forest, and RNNs in patient treatment and clinical results by using different datasets in the medical field. Hence for interpretative purposes, Logistic Regression was accurate with a mean of 0.78 and ROC-AUC of 0.82 that is acceptable for making binary classifications. CNNs outperformed other models in comparing the medical images and achieved an accuracy of 92% as well as ROC-AUC of 95%, which points to high diagnostic capacity. Cohort: Random Forest showed high accuracy, 85% and high ROC-AUC at 88% to big miracle performance in handling features from high dimensionality. There was a great performance of RNN when testing time-series data with accuracy of 83% and ROC-AUC of 86%. As noticed while comparing the results with the traditional approaches and other related research, all AI incorporations exhibited better outcomes in our work. The study points to the future of AI in improving patient diagnosis and addressing disparities in healthcare, but it also sheds light on issues like patient data protection and unfairness in AI models. The real push should be towards specific ethical aspects as well as teaming up with experts from other fields for the proper AI implementation into clinical practice.
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