ArticleNature communications2025
Intricacies of human-AI interaction in dynamic decision-making for precision oncology.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- CATalyze-AI: accelerating referral for thrombectomy in acute stroke patients using an AI-based software. A prospective, multicentre, quasi-experimental cohort study.European stroke journal · 2026Article
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Human-AI Collaboration in Healthcare: A Scoping Review.NPJ digital medicine · 2026Article
- A scoping review of human-AI collaboration patterns and task divisions in healthcare applications.NPJ digital medicine · 2026Article
- Adaptive yet suboptimal integration of advice in decision-making.Communications psychology · 2026Article
- The current and future landscape of AI foundation models for cancer management.Nature communications · 2026Article
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2026Article
- Artificial intelligence applications in adaptive radiotherapy-a narrative review.Translational cancer research · 2026Review
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Process Systems Engineering in Precision Medicine: Opportunities in Autologous CAR-T Therapy.Engineering in life sciences · 2026Review
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.Frontiers in immunology · 2026Review
- LLM-powered TNM staging of neuroendocrine tumors from PET/CT reports.BMC medical imaging · 2025Article
- Unitary Entities Are the True "Atoms".Entropy (Basel, Switzerland) · 2025Review
- Revolutionizing gastrointestinal cancer research with artificial intelligence: From precision patient stratification to real-world evidence.World journal of gastrointestinal oncology · 2025Review
- The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma.Frontiers in medicine · 2025Review
Corrections and comments
- Update of
Authors and funding
22 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.