Trust and Consent Challenges in AI-Supported Surgical Planning: A Qualitative Study

Zeng Wenchao; Chrismas Gideon Bangun; Arna Fransisca Millyanti Purba

doi:10.61919/8xsycj49

Authors

Zeng Wenchao Department of Surgery, Universitas Prima, Indonesia Author
Chrismas Gideon Bangun Department of Surgery, Universitas Prima, Indonesia Author
Arna Fransisca Millyanti Purba Department of Surgery, Universitas Prima, Indonesia Author

DOI:

https://doi.org/10.61919/8xsycj49

Keywords:

artificial intelligence; surgical planning; informed consent; trust; accountability; qualitative study.

Abstract

Background: Artificial intelligence is increasingly used in surgical planning through imaging interpretation, anatomical modelling, risk estimation and decision support. Although these tools may support clinical preparation, they create consent challenges when patients are not told how AI influenced the plan, how their data are used or who remains responsible for errors. Qualitative inquiry is needed to understand how patients interpret AI involvement, trust and accountability in surgical consent. Objective: To explore patient perspectives on trust and consent in AI-supported surgical planning, focusing on disclosure, explainability, surgeon responsibility, privacy, fairness and accountability. Methods: This qualitative descriptive study used semi-structured participant accounts from 12 purposively selected surgical patients and patient-related stakeholders. Participants represented variation in surgical experience, digital confidence, family decision-making roles and attitudes toward AI-supported planning. Data were analyzed using reflexive thematic analysis informed by sensitising concepts from AI ethics and inductive interpretation of participant concerns. Results: Six themes were identified: unclear boundaries between surgeon judgement and AI advice; consent without meaningful AI disclosure; explainability and patient understanding; data privacy, bias and fairness concerns; accountability for planning errors; and relational trust in the surgeon as the consent anchor. Participants did not reject AI-supported planning in principle, but acceptance depended on plain-language disclosure, visible surgeon responsibility, transparent data governance and clear institutional accountability. Conclusion: Consent for AI-supported surgical planning should be treated as a patient-centred communication process. Sustainable implementation requires proportionate AI disclosure, clinician explanation, documentation of AI use, governance safeguards and explicit reassurance that surgeons remain responsible for final clinical decisions.

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