Real-world artificial intelligence use in oncology practice: High clinician verification amid critical governance gaps.

Abstract

353 Background: Artificial intelligence (AI) tools are rapidly entering oncology practice. However, real-world data describing how clinicians use AI—particularly outside institution-approved systems—remain limited. Characterizing adoption, verification, and responsibility is essential for safe implementation. Methods: We conducted a voluntary, anonymous cross-sectional survey of U.S. oncology clinicians assessing real-world AI use. Domains included clinician characteristics, institutional access, independent AI use, verification behavior, governance, and responses to a standardized clinical scenario. Descriptive statistics were performed. Results: Thirty-one clinicians completed the survey, including hematology–oncology fellows (45%) and attending oncologists (29%), across academic and community settings. Despite limited institutional access, 97% reported independent clinical AI use. Over 80% consistently removed patient identifiers prior to use. AI literacy was high, with recognition of probabilistic outputs and limited generalizability. When AI conflicted with clinical judgment, 87% always or usually independently verified outputs. In a standardized scenario, confidence in AI recommendations varied widely. Responsibility for AI-related errors was attributed to clinicians alone or shared with institutions and vendors. Notably, 68% reported no clear institutional policies governing AI in clinical workflows. Conclusions: Clinicians are integrating AI largely outside formal oversight while maintaining high verification rates. These findings support the need for clinician-led governance frameworks aligning AI use with patient safety and institutional responsibility. AI governance–adoption gap in oncology practice (N = 31). Characteristic n (%) Hematology–Oncology Fellows 14 (45) Attending Medical Oncologists 9 (29) Academic Practice Setting 16 (52) Independent AI Use for Clinical Work 30 (97) Access to Approved Institutional AI Tools 13 (42) No Access or Unsure 18 (58) Clear Institutional AI Policies Exist 10 (32) No Clear AI Governance 21 (68) Using AI Without Institutional Oversight* 20 (65) *Defined as clinicians reporting either no institutional AI policies or no access to approved AI tools while using AI clinically.