Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 29, 2025
Date Accepted: Apr 2, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
“Because human interaction still needs to be there” – Expectations and needs of kidney transplant patients and their support persons regarding AI-based DSS: A qualitative study at a tertiary care center
ABSTRACT
Background:
Artificial intelligence (AI) is increasingly used to support many areas in medicine, including clinical decision-making. Although AI-based decision support systems (DSS) offer benefits such as early risk detection and treatment optimization, little is known about how patients and their support persons (SPs) perceive the role and impact of these tools.
Objective:
This study explores expectations and informational needs of kidney transplant patients and SPs regarding the use of AI-assisted DSS in clinical care and its impact on shared decision-making (SDM).
Methods:
As part of a longitudinal qualitative study, 36 semi-structured interviews with kidney transplant patients and their SPs were conducted at a German kidney transplant centre (KTC). Participants were asked about their views on the role and expectation of AI in their post-transplant care and how they perceive trust, communication and (shared) decision-making with the use of AI. Interviews were transcribed, pseudonymized, and analysed using framework analysis.
Results:
Participants valued AI for identifying risks related to transplant loss, rejection, and infections and supporting physicians with data-driven treatment recommendations but emphasized that final decisions should remain with physicians. Many feared AI could depersonalize care and negatively impact physician-patient communication due to the lack of “human touch”. Participants expressed limited understanding of how AI-DSS work and a need for simple, accessible explanations on the operations of AI-based DSS (e.g. informational leaflets). While most participants doubted that AI could replicate empathy, some acknowledged that AI might be perceived as more attentive and caring than rushed physicians, who often have a lack of time for adequate communication with their patients.
Conclusions:
Patients and SPs advocate for the use of AI in follow-up care when it enhances rather than replaces human decision-making. Trust and acceptance hinge on transparency, accountability, and preserving the “human touch” in clinical encounters. Educational tools are needed to better inform patients about how AI could be used to support optimal care.
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