Intricacies of human–AI interaction in dynamic decision-making for precision oncology
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
- Brian D. Gonzalez (ORCID: https://orcid.org/0000-0001-5108-5735)
- Sean R. Miller (ORCID: https://orcid.org/0000-0001-5059-2209)
- Issam El Naqa (ORCID: https://orcid.org/0000-0001-6023-1132)
- Dipesh Niraula (ORCID: https://orcid.org/0000-0002-2245-8536)
- Russell F. Palm (ORCID: https://orcid.org/0000-0002-0448-8715)
- Yi Luo (ORCID: https://orcid.org/0000-0003-2519-5900)
- Jessica M. Frakes (ORCID: https://orcid.org/0000-0002-1901-0933)
- Anupam Rishi (ORCID: https://orcid.org/0000-0001-5258-9601)
- Samuel N. Regan (ORCID: https://orcid.org/0000-0002-5449-8348)
- Martha M. Matuszak (ORCID: https://orcid.org/0000-0002-4129-1030)
- Randall K. Ten Haken (ORCID: https://orcid.org/0000-0003-1331-0297)
- Ya-Yu Tsai (ORCID: https://orcid.org/0000-0002-0621-9519)
- Ivo D. Dinov (ORCID: https://orcid.org/0000-0003-3825-4375)
- Matthew N. Mills (ORCID: https://orcid.org/0000-0002-8483-105X)
- Alex K. Bryant (ORCID: https://orcid.org/0000-0003-0194-8381)
- Jamalina Jamaluddin (ORCID: https://orcid.org/0000-0002-6534-8388)
- Michael Dykstra (ORCID: https://orcid.org/0000-0003-0834-7669)
- Javier F. Torres–Roca (ORCID: https://orcid.org/0000-0001-5169-2121)
- Kyle Clifford Cuneo (ORCID: https://orcid.org/0000-0002-8264-6483)
- Jionghua Jin
- Casey Liveringhouse
- Thomas J. Dilling (ORCID: https://orcid.org/0000-0003-0257-6989)
Institutions
- University of Michigan (US)
- Moffitt Cancer Center (US)
- VA Ann Arbor Healthcare System (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1038/s41467-024-55259-x
- Citations
- 27
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 66.99
Funders
- U.S. Department of Defense
- Novocure
- National Comprehensive Cancer Network
- National Institutes of Health
- National Cancer Institute