Intricacies of human–AI interaction in dynamic decision-making for precision oncology
Dipesh Niraula; Kyle C. Cuneo; Ivo D. Dinov; Brian D. Gonzalez; Jamalina Jamaluddin; Jionghua Jin; Yi Luo; M.M. Matuszak · 2025 · Nature Communications
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
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
Abstract by Dipesh Niraula; Kyle C. Cuneo; Ivo D. Dinov; Brian D. Gonzalez; Jamalina Jamaluddin; Jionghua Jin; Yi Luo; M.M. Matuszak, Nature Communications (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41467-024-55259-x