Intricacies of human–AI interaction in dynamic decision-making for precision oncology

Dipesh Niraula (), Kyle C. Cuneo, Ivo D. Dinov, Brian D. Gonzalez, Jamalina B. Jamaluddin, Jionghua Judy Jin, Yi Luo, Martha M. Matuszak, Randall K. Ten Haken, Alex K. Bryant, Thomas J. Dilling, Michael P. Dykstra, Jessica M. Frakes, Casey L. Liveringhouse, Sean R. Miller, Matthew N. Mills, Russell F. Palm, Samuel N. Regan, Anupam Rishi, Javier F. Torres-Roca, Hsiang-Hsuan Michael Yu and Issam El Naqa ()
Additional contact information
Dipesh Niraula: Moffitt Cancer Center
Kyle C. Cuneo: University of Michigan
Ivo D. Dinov: University of Michigan
Brian D. Gonzalez: Moffitt Cancer Center
Jamalina B. Jamaluddin: Moffitt Cancer Center
Jionghua Judy Jin: University of Michigan
Yi Luo: Moffitt Cancer Center
Martha M. Matuszak: University of Michigan
Randall K. Ten Haken: University of Michigan
Alex K. Bryant: University of Michigan
Thomas J. Dilling: H. Lee Moffitt Cancer Center & Research Institute
Michael P. Dykstra: University of Michigan
Jessica M. Frakes: H. Lee Moffitt Cancer Center & Research Institute
Casey L. Liveringhouse: H. Lee Moffitt Cancer Center & Research Institute
Sean R. Miller: University of Michigan
Matthew N. Mills: H. Lee Moffitt Cancer Center & Research Institute
Russell F. Palm: H. Lee Moffitt Cancer Center & Research Institute
Samuel N. Regan: University of Michigan
Anupam Rishi: H. Lee Moffitt Cancer Center & Research Institute
Javier F. Torres-Roca: H. Lee Moffitt Cancer Center & Research Institute
Hsiang-Hsuan Michael Yu: H. Lee Moffitt Cancer Center & Research Institute
Issam El Naqa: Moffitt Cancer Center

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals’ cancer progression for effective personalized care. However, AI’s imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human–AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma. We investigated two levels of collaborative behavior: model-agnostic and model-specific; and found that Human–AI interaction is multifactorial and depends on the complex interrelationship between prior knowledge and preferences, patient’s state, disease site, treatment modality, model transparency, and AI’s learned behavior and biases. In summary, some clinicians may disregard AI recommendations due to skepticism; others will critically analyze AI recommendations on a case-by-case basis; clinicians will adjust their decisions if they find AI recommendations beneficial to patients; and clinician will disregard AI recommendations if deemed harmful or suboptimal and seek alternatives.

Date: 2025
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DOI: 10.1038/s41467-024-55259-x

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