Multi-Objective Optimization: Applying P-Algorithm to Evaluate the Advantage of AI for Early Disease Detection in the Medical Sector

Healthcare Patients are concerned about their information being hacked or misused in any way because AI systems require vast volumes of data, including private medical records. By increasing accuracy, efficiency, and accessibility, artificial intelligence (AI) technologies are being increasingly incorporated for early disease detection, leading to significant improvements in medical evaluation and therapy planning. The number of patients entering the majority of hospitals overwhelms them, resulting in lengthy wait times, less individualized treatment, and potentially subpar diagnostic testing for the detection of illness early. The purpose of this research is to investigate how artificial intelligence (AI) might improve early disease detection, with an emphasis on higher patient satisfaction by using a new P-algorithm. The study implements a novel P-algorithm to enhance accuracy and patient outcomes in medical services. The findings have a substantial and powerful influence on the contribution that might improve early disease detection, emphasizing higher patient satisfaction by using a new P-algorithm. The current study contributed that by overcoming these obstacles, AI systems that employ P-algorithms would be able to greatly enhance early disease detection and, as a result, further enhance the patient’s access to timely and personalized healthcare services while maintaining adherence to privacy, trust, and fairness principles.