Clinical Report: Recognizing the Significance of Divergence in Clinical Human–AI Interactions

Overview

The article argues that differences between clinician and AI outputs should be treated as human–AI divergence, not simply as disagreement or an error to correct. It proposes making these events visible and documenting the interaction, including the distinct information available to each party and how clinicians engage with AI reasoning.

Background

Clinical AI interfaces commonly present an output for clinicians to accept, override, or modify, leaving the interaction behind that decision largely undocumented. Clinicians remain responsible for recommendations and explanations to patients, while navigating autonomy, accountability, and liability. The source notes that clinicians may lack practical means to act as a human in the loop, and that explainability has not consistently improved decisions and can foster false confidence in erroneous outputs. A cited review of AI ethics in nursing and midwifery also reports concern that efficiency-focused AI may make relational dimensions of care less visible.

Data Highlights

The supplied article excerpt contains no numerical trial results. It describes three dimensions of human–AI divergence visibility: structural (workflow and interface), informational (what each party knows), and behavioral (clinician engagement with AI reasoning).

Key Findings

• The article defines a divergence event as a difference between clinician and AI outputs and uses “divergence” rather than “disagreement” because the parties may have access to different information.
• Clinicians contribute bedside assessment, communication with families, and, during procedures, haptic, visual, and anatomical-variant information that AI systems may not readily access or integrate.
• AI systems process structured or semi-structured health-record data and may draw on patterns from large numbers of prior cases or external databases.
• Divergence may arise from asymmetric information, genuine differences in interpreting shared evidence, or an error by either party; seeking another opinion may not address divergence caused by information asymmetry.
• The article identifies possible clinician responses including evaluating which assessment is correct, combining signals, uncritically accepting AI output (automation bias), or reflexively overriding it (automation neglect).
• Related work describes a collaborative human–AI engagement framework, BRACE, that centers the clinician–AI interaction rather than model output; the supplied abstract does not provide outcome data.

Clinical Implications

The article proposes documenting the interaction itself, including what the clinician and AI each contributed when their outputs diverged, rather than recording only the final decision. It also emphasizes that clinicians must determine what to recommend and explain to patients while considering autonomy, accountability, and liability.

Conclusion

Human–AI divergence is presented as a high-signal point for examining how clinical and AI reasoning differ, not merely as a contest over which output is correct. The article calls for visibility into the interaction so that its contributions and sources of divergence can be examined.

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9. AMA policies to ensure AI supports—not replaces—physician judgment
10. ACR Approves First Practice Parameter for Imaging Artificial Intelligence
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