Agentic AI Systems in Organ Health Management: Early Detection of Rejection in Transplant Patients

Abstract

The purpose of this essay is to examine the integration of agentic artificial intelligence (AI) systems in the management of organ health. Aspects of this analysis include the importance of custom software design and the diagnostic abilities of these systems, as well as the potential for creating an AI system that promotes a symbiotic doctor-AI-patient relationship. Although discussions are generally theoretical, examples will be used from ongoing research focusing on the early detection of rejection in patients with heart transplants, with these patients referred to as X-patients throughout this essay. Organ transplantations allow patients who would have otherwise perished to continue living, but make their immune systems suppress in order to preserve the alien tissues. This non-standard immune state puts them at risk for other diseases, which is why monitoring their health carefully is crucial. However, due to living an active life after recovery, X-patients may occasionally engage in risky behaviors without sufficient regard for their health. In consequence, they are not supposed to discontinue undergoing medical examinations after the end of the postoperative recovery period. The custom-designed smart system integrates multiple AI applications to facilitate the convenient monitoring of X-patient organ health throughout the day by providing them with continuously updated personalized feedback based on the patient’s current state and causes of concern. By creating and maintaining this system, the wellbeing of X-patients is easily safeguarded while reducing the volume of work for the overburdened entourage of these patients.

In the first part of this comparative analysis, the early detection of rejection in X-patient heart transplants will be examined. Research pursuing this analysis has led to the design and disconnected self-usage of two different custom smart AI systems. While one is entirely self-contained with no direct patient-AI doctor interaction, the other involves diagnostic responses from the AI. There will be a focus on factors causing symptoms to arise, the internal or external sources these symptoms arise from, and why potential warning signs must be attributed to heart rejection. This last issue has fostered the need for ongoing image recognition-based research, and the observational or perceived difference in symptom effect will be reflected in the intelligent AI system’s feedback. The larger portion of this text will focus on the later-designed AI system due to possessing a greater degree of development. However, before examining the feedback, necessary information required for the understanding of the AI functioning and entourage approach must be presented.