Evidence map›Paper›PMID 41647297›Full record

ArticleESMO real world data and digital oncology2024

Impact of artificial intelligence in transforming the doctor-cancer patient relationship.

P-E Heudel, H Crochet, J-Y Blay

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

P-E HeudelDepartment of Medical Oncology, Centre Léon Bérard, Lyon.
H CrochetData and Artificial Intelligence Team, Centre Léon Bérard, Lyon.
J-Y BlayDepartment of Medical Oncology, Centre Léon Bérard, Lyon.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencecancerdoctor–patient relationshipshared decision making

Identifiers

PMID41647297
PMCPMC12836740

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.