When doctors and AI medical opinions diverge, humans must question the difference, according to experts
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The human doctor and the artificial intelligence (AI) tool they are using more and more frequently are not always going to agree.
Carefully setting up a process of parallel assessments starting from the very beginning of data collection is the way to make the most of both human and machine when diagnosis and prognosis do not completely align, according to a new paper in the journal npj Digital Medicine.
"Realizing the value of the divergence event in clinical human-AI interaction" assesses how best to reconcile the two "experts." The lead author is Charles E. Binkley, M.D., FACS, HEC-C, the director of AI ethics and quality for Hackensack Meridian Health, as well as professor of surgery and chair of the Faculty Assembly for the Hackensack Meridian School of Medicine.
"Clinicians are told to be the 'human in the loop' but are typically given no method for doing so beyond 'exercising their judgment,'" write the authors. "Efforts to make AI output both more technically explainable and human interpretable have not directly correlated with improved clinical decisions and have sometimes created false confidence in incorrect AI output."
Different kinds of clinical knowledge
The disagreements should instead be called "divergence events," since there are nuanced differences between what the human and the machine are assessing, according to the authors.
For instance, the physician actually speaking to the patient "brings a dynamic bedside gestalt" from seeing the patient "in person" and speaking to family members. Meanwhile, the AI model has an enormous body of data from electronic health records and consequent pattern recognition from thousands or millions of prior cases, according to the authors.
"Divergence visibility" is the key factor in reconciling the two, according to the team of experts, which included experts from the University of Maryland School of Medicine, Beth Israel Deaconess Medical Center Harvard Medical School, New York Presbyterian/Columbia University Irving Medical Center, and Columbia University Vagelos College of Physicians and Surgeons.
Independent assessments before reconciliation
Various models of using AI as a tool posit potentially using it before the clinician fully assesses the case in its entirety, or using AI as a primary assessment and diagnostic tool.
Sequential output (clinician forms a judgment, then considers the AI output, and finally revisits the clinical evidence and their reasoning if there is divergence before making a final call) may mitigate liability and also offer potential protection against the decline of clinical reasoning skills.
An example: using the machine as a second and separate reader of breast cancer screening images. The two totally independent reads can then use an explicit reconciliation step to pinpoint exactly where the two opinions diverge. By recording that divergence point and interrogating it, the clinician actively compares the reasoning and homes in on resolving the disagreement in a minute way. Following this structure could enable both the clinician and the machine to learn from each other at the point of divergence.
"The most consequential moments in clinical AI are the ones where the clinician and the AI system diverge," the authors write. "How the field treats those divergence moments (either as only adjudications between two outputs, or as insightful interactions between two kinds of knowledge) will determine whether AI systems become a true partner for safer clinical decisions.
"Human-AI interaction divergence visibility articulates a need to make the human-AI interactions visible to the clinician in real time and to the field over time, to truly advance patient care," they add.
Publication details
Florence X. Doo et al., Realizing the value of the divergence event in clinical human-AI interaction, npj Digital Medicine (2026). DOI: 10.1038/s41746-026-03394-8
Journal information: npj Digital Medicine
Provided by Hackensack Meridian Health Who's behind this story?
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