Application of Artificial Intelligence Techniques for Improving Diagnostic Accuracy and Treatment Outcomes in Healthcare Systems

Artificial Intelligence (AI) stands to change how we operate in healthcare systems radically. Processes become less error-prone and biased; gaps in diagnoses become less frequent; and patient care and managing patient traction become considerably better. The purpose of this study is to highlight the use of AI within healthcare systems to deliver better diagnoses and optimal care. The research will primarily focus on clinical datasets to support predictive and diagnostic analyses for the treatment regimen. A research methodology was adopted and modified within the parameters of published clinical datasets and hospital dataset pieces. With the use of almost all AI models (Support Vector Machine (SVM), Random Forest, Convolutional Neural Networks (CNN), etc.), the performance of the models that created the datasets for diagnostic and treatment prediction was evaluated using analysis of various performance statistical indicators such as accuracy, precision, sensitivity, specificity, and F1-score. The research thus created an environment in which almost all AI-assisted diagnostic models could be applied to clinical patients in the lab. The accuracy of diagnoses in this case was 94.4%, compared with 82.4% based on conventional diagnostics. The creation of an optimal AI-based diagnostic model and treatment planning resulted in a 27% reduction in diagnostic delay and a 31% increase in patient care outcomes. The research elucidated the extensive benefits that patients and healthcare systems obtain when AI is a part of the treatment model. The study shows that AI techniques significantly improve diagnostic accuracy, clinical productivity, and patient-oriented outcomes. AI technologies can be embedded in healthcare systems to improve and support precision medicine and further develop Health and Life Sciences through evidence-based and smart clinical practices.