Abstract
AI holds significant promise for guiding clinical decisions in end-stage organ failure, where treatment options now include medical management, transplantation, mechanical support devices, and palliative care. This paper discusses current applications of AI in healthcare, emphasizing the complex decision-making necessary for patients with organ failure. It outlines how AI can support risk stratification, patient selection, and outcome prediction, particularly in transplantation practices that increasingly rely on robust data to inform care pathways. By analyzing large datasets from electronic health records, imaging, and patient-reported outcomes, AI can help physicians forecast long-term survival and quality of life, and potentially assist clinicians in modifying treatment strategies before adverse trajectories take hold. There is a need for standardized, high-quality data, rigorous validation, and transparent algorithms to mitigate biases that could exacerbate disparities in care. Ethical considerations demand attention to equitable access, patient privacy, and the preservation of the human element in patient-clinician relationships. Patients generally view AI with cautious optimism, recognizing its potential to augment care but also voicing concerns about data protection and the risk of losing compassionate, personal care. Importantly, AI can help with "alignment," or fitting treatment recommendations to patients' values, and promote sustainable and patient-centered outcomes. Ultimately, the successful integration of AI into daily practice requires multidisciplinary collaboration among developers, clinicians, ethicists, and regulators. Deep stakeholder engagement, continuous algorithmic refinement, and user-friendly design are pivotal to ensuring that AI serves as a practical decision-support tool that complements, rather than replaces, clinical expertise and shared decision-making.