ReviewJCO oncology practice2025
Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients.
Review in JCO oncology practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Capacity of Understanding the Future Approaches in Cancer Treatment by Multiple Models of Artificial Intelligence.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Analysis of Large Language Model Decision Making in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Early Breast Cancer.JCO clinical cancer informatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Much work has been published on artificial intelligence (AI) and oncology, with many focusing on an algorithm perspective. However, very few perspective articles have explicitly discussed the role of AI in oncology from the perspectives of the stakeholders-the clinicians and the patients. In this article, we delve into the opportunities of AI in oncology from the clinician's and patient's lens. From the clinician's perspective, we discuss reducing burnout, enhancing decision making, and leveraging vast data sets to provide evidence-based recommendations, eventually affecting diagnostic accuracy and treatment planning. From the patient's perspective, we discuss AI virtual concierge, which could improve the cancer care journey by facilitating patient education, mental health support, and personalized lifestyle wellness recommendations promoting a holistic approach to care. We aim to highlight the stakeholders' unmet needs and guide institutions to create innovative AI solutions in oncology. By addressing these perspectives, our article aims to bridge the gap between technological research advancements and their real-world AI-focused clinical applications in cancer care. Understanding and prioritizing the needs of the stakeholders will foster the development of impactful AI tools and intentional utilization of such technology, with an aim for clinical implementation and integration into workflows.
Indexed as
Identifiers
What OpenQuestion holds
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.