Human-AI Interaction in Kidney Transplant Decision Support Systems: Qualitative Study of Patient and Support Person Expectations

Clinical Snapshot

Key Findings

• P Value: Not applicable for qualitative research

• Effect Size: 78% (n=28/36) expressed concern about AI depersonalizing care

• Primary Outcome: Patient and support person expectations and perceptions of AI-assisted decision support systems in post-transplant care

• Nnt Or Sensitivity: Key themes: AI should augment not replace physicians, need for transparency and education, preservation of human touch essential

• Confidence Interval: Not applicable for qualitative research

Clinical Application

Findings suggest AI-DSS implementation feasible if designed with patient-centered principles, transparency, and physician oversight Relevant to Australian transplant programs; findings align with TGA emphasis on transparency in medical devices and RACGP patient-centered care principles. May inform development of AI-DSS under Australian regulatory framework Kidney transplant recipients and their support networks, potentially applicable to other chronic disease populations requiring long-term monitoring

Abstract

BACKGROUND: Artificial intelligence (AI) is increasingly applied in medicine, including clinical decision-making. AI-based decision support systems (DSS) can enhance early risk detection and treatment optimization. However, the perspectives of patients and their support persons on AI-assisted DSS in clinical care, particularly regarding shared decision-making (SDM), remain underexplored. OBJECTIVE: This study investigates the expectations, informational needs, and perceptions of patients who underwent kidney transplantation and their support persons regarding AI-assisted DSS and its influence on SDM in posttransplant care. METHODS: In a longitudinal qualitative study, 36 semistructured interviews were conducted with patients who underwent kidney transplantation and their support persons at a German kidney transplant center. Participants were asked about their views on AI's role in follow-up care, its impact on communication, trust, and decision-making, as well as their informational needs regarding AI-assisted DSS. Interviews were transcribed, pseudonymized, and analyzed using framework analysis. RESULTS: Participants recognized AI's potential to support clinicians by identifying risks of transplant loss, rejection, and infection, and by providing data-driven treatment recommendations. However, they emphasized that final decisions should remain with physicians. A majority of participants (n=28, 78%) expressed concern that AI might depersonalize care and diminish physician-patient communication due to a lack of "human touch." Participants demonstrated limited understanding of AI-based DSS functionality and highlighted the need for simple, accessible educational materials (eg, leaflets) explaining AI operations. While most doubted AI could replicate human empathy, some acknowledged that AI might be perceived as more attentive than time-pressured physicians, offering consistent monitoring and support. Participants consistently stressed that AI should augment, not replace, clinical decision-making. CONCLUSIONS: Patients who underwent kidney transplantation and support persons endorse the integration of AI in follow-up care when it enhances clinical decision-making without supplanting the physician's role. Acceptance and trust depend on transparency, accountability, and preserving the "human touch" in care. The development of educational tools to communicate AI functions and limitations is crucial to empower patients and support persons in SDM processes and to ensure AI complements, rather than undermines, patient-centered care.

References

1. 1.Sassi, Z., Eickmann, S., Roller, R., Osmanodja, B., Spencker, J. J., Ömeroğlu, Ö. E., Burchardt, A., Hahn, M., Dabrock, P., Möller, S., Budde, K., & Herrmann, A. (2026). Human-AI interaction in kidney transplant decision support systems: Qualitative study of patient and support person expectations. Journal of Medical Internet Research. https://doi.org/10.2196/83195