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

Journal: Nature communications
PMID: 39881134

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.

Authors

  • Dipesh Niraula
    Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States.
  • Kyle C Cuneo
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Ivo D Dinov
    Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; Statistics Online Computational Resource, Department of Health Behavior and Biological, University of Michigan, Ann Arbor, MI, USA.
  • Brian D Gonzalez
    Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida.
  • Jamalina B Jamaluddin
    Department of Nuclear Engineering and Radiological Sciences, Moffitt Cancer Center, Tampa, FL, USA.
  • Jionghua Judy Jin
    Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Martha M Matuszak
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America.
  • Randall K Ten Haken
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Alex K Bryant
    Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
  • Thomas J Dilling
    Moffitt Cancer Center, Tampa, FL, USA. Electronic address: thomas.dilling@moffitt.org.
  • Michael P Dykstra
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Jessica M Frakes
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Casey L Liveringhouse
    Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Sean R Miller
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Matthew N Mills
    Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Russell F Palm
    Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Samuel N Regan
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Anupam Rishi
    Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Javier F Torres-Roca
    Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Hsiang-Hsuan Michael Yu
    Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Issam El Naqa
    Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida.

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