AI APPLICATIONS IN HEALTHCARE: PREDICTIVE DIAGNOSIS AND TREATMENT: LEVERAGING MACHINE LEARNING FOR PRECISION MEDICINE AND PATIENT CARE

Abstract

The Artificial Intelligence (AI) has resulted in disruptive technology in the healthcare sector due to detailed explanations of flow model logic and precise treatment planning capabilities. When healthcare deals with the assistance of machine learning (ML) algorithms, it can not only predict the presence of certain diseases earlier but also analyze the multifaceted medical data and tailor treatment of the specific patient comparative model accuracy in predictive diagnosis. The present paper will discuss how AI-based predictive models can be advantageous in confusion matrix visualization for disease classification and patient outcomes in addition to being utilized to enhance the process of clinical decision-making. It was possible with the assistance of supervised and unsupervised learning approaches to process big amounts of patient data predetermining the pattern of illness progression, reaction to therapy, and potential complications. The findings are that AI technology has the capability of diagnosing the early stage of the diseases, such as diabetes, heart diseases, as well as certain forms of cancers with a high degree of accuracy approximating 92. However, there are still limitations to the area of data privacy, bias, interoperability, and generalization of the models, making it impossible to implement this technology in the large scale in the practice clinics. Finally, treatment recommendations are derived using probabilistic inference. Future research should involve the consideration of the investigation to develop explicable AI structures, shared data models, and integrated human-AI cooperation systems that ensure transparency, ethical stipulation, and universalism in precision medicine temporal performance trend of predictive diagnosis across validation iterations.