When Doctors and AI Medical Opinions Diverge Humans Must Question the Difference

A paper published in npj digital medicine, a Nature journal, led by Charles E. Binkley, M.D., director of AI Ethics and Quality at Hackensack Meridian Health, proposes treating disagreements between clinicians and AI diagnostic tools as "divergence events" and reconciling them through parallel, independent assessments. The authors recommend that doctors form their own judgment before reviewing AI output, then revisit the clinical evidence and their reasoning where the two diverge, citing independent double-reading of breast cancer screening images as an example. The paper argues that "divergence visibility" is the key factor in reconciling human and machine assessments, noting that clinicians are told to be the "human in the loop" but are typically given no method beyond "exercising their judgment.

Key Takeaways - A new study explores how doctors and artificial intelligence AI can work together when they reach different conclusions about a patient's diagnosis or prognosis. - Researchers recommend that doctors make their own assessments before reviewing AI findings to help preserve independent clinical judgment. - The study calls disagreements between doctors and AI "divergence events" and suggests reviewing these differences to support clinical decisions. - Doctors bring firsthand patient observations, while AI can identify patterns across large amounts of medical data. - Charles E. Binkley, M.D., of Hackensack Meridian Health , led the research, published in the journal npj digital medicine. AI generated. The human doctor and the artificial intelligence AI tool they are using more and more frequently are not going to always 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 , a Nature publication https://www.nature.com/articles/s41746-026-03394-8 . "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." 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 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. 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.