ArticleESMO real world data and digital oncology2024
Impact of artificial intelligence in transforming the doctor-cancer patient relationship.
Article 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 11 papers.
What it found
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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
11 citing papers in PubMed.
- The Use of Artificial Intelligence Chatbots by Newly Diagnosed Cancer Patients: A Descriptive Phenomenological Study.Current oncology (Toronto, Ont.) · 2026Article
- Artificial intelligence in oncology: empowering clinicians for responsible integration.ESMO real world data and digital oncology · 2026Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Cancer patient navigation interventions improving timeliness from diagnosis to first treatment in oncology care: A scoping review.Japan journal of nursing science : JJNS · 2026Article
- Article
- International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery.Scientific reports · 2025Article
- Attitudes toward the use of ChatGPT to seek oncological information: a qualitative study.Frontiers in psychology · 2025Article
- Artificial intelligence and science of patient input: a perspective from people with multiple sclerosis.Frontiers in immunology · 2025Article
- Artificial intelligence in oncology: ensuring safe and effective integration of language models in clinical practice.The Lancet regional health. Europe · 2024Review
- Transforming breast cancer management with real-world data and artificial intelligence.ESMO real world data and digital oncology · 2024Article
- MGMTai: O6-methylguanine-DNA methyltransferase (Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The integration of Artificial Intelligence (AI) in oncology is a developing field, impacting the doctor-patient relationship and the efficacy of cancer care. While AI's role in improving clinical efficiency and personalized care through the analysis of vast medical datasets is acknowledged, its full scope and impact are not yet completely understood. Materials and methods: This article synthesizes empirical studies and expert opinions to offer a comprehensive understanding of AI's current role in oncology. The methodology focuses on exploring the balance between technological advancements and the essential elements of patient-centered care. Results: The paper hypothesizes that AI can enhance the quality of cancer care, but notes challenges such as potential depersonalization, data privacy issues, and ethical dilemmas. It also highlights AI's potential in facilitating Shared Decision-Making, empowering patients and assisting oncologists in making more informed decisions. However, the risk of AI-driven paternalism and the need for balancing AI recommendations with patient autonomy are discussed. Conclusions: AI holds significant potential to transform cancer care. The paper concludes that for AI to be beneficial, its integration should be collaborative and patient-centered, ensuring that technological advancements support and enhance the quality of the doctor-patient relationship, rather than undermining it. The article emphasizes the importance of transparent communication, patient education about AI, and the need for oncologists to effectively understand and convey AI-generated data.
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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.