ReviewESMO real world data and digital oncology2024
Oncology education in the age of artificial intelligence.
Review in ESMO real world data and digital oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Artificial intelligence in oncology: empowering clinicians for responsible integration.ESMO real world data and digital oncology · 2026Review
- The Lisbon Manifesto on Precision Cancer Education: A Call to Transform Cancer Education from Personalized Prevention to Personalized Care.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Evidence-Based Analysis of AI Chatbots in Oncology Patient Education: Implications for Trust, Perceived Realness, and Misinformation Management.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Review
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Evaluating the combined efficacy of Telisotuzumab Vedotin and artificial intelligence in the treatment of non-squamous non-small cell lung cancer: a narrative review focusing on pharmaceutical and technical insights.Frontiers in oncology · 2025Review
- AI-Powered Insights into Drug Resistance in Gastric Cancer: A Path Toward Precision Therapy.Iranian journal of pharmaceutical research : IJPRReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancements in artificial intelligence (AI) technology have broad implications for the clinical practice of oncology. AI methods enable us to analyze unstructured patient data in a quantitative way, at scale. AI can also help to manage the exponentially growing amount of specialist knowledge, for example, by parsing information from medical guidelines. As oncologists, we will increasingly interact with AI systems in clinical routine and research. These technical developments will require a reshaping of oncology education, which needs to include the development of 'AI literacy' as a core aim. Henceforth, oncologists require an understanding of the principles of AI, and the capabilities to adapt and use AI for clinical and research tasks. They should also be able to interpret AI-generated data effectively, especially when communicating with patients who will increasingly use AI tools. Oncologists who understand the fundamental concepts of AI, its limitations, and capabilities will be able to come up with new ideas and use cases, for example, in designing clinical trials. Currently, postgraduate oncology education lacks comprehensive curricula addressing AI integration. We propose that the scientific community, in particular academic institutions, professional societies, and policymakers, should prioritize implementing AI literacy within oncology curricula.
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.