Evidence map›Paper›PMID 40838109›Full record

ArticleMedical review (2021)2025

Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacy.

Junyi Chen, Xinlin Zhu, Jian-Yue Jin, Feng-Ming Spring Kong, Gen Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical review (2021), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Junyi ChenState Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing, China.
Xinlin ZhuState Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing, China.
Jian-Yue JinSchool of Biomedical Engineering, Capital Medical University, Beijing, China.
Feng-Ming Spring KongDepartment of Clinical Oncology, University of Hong Kong, Hong Kong, China.
Gen YangState Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing, China.ORCID https://orcid.org/0000-0002-0695-5583

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer remains a substantial global health challenge, with steadily increasing incidence rates. Radiotherapy (RT) is a crucial component in cancer treatment. Nevertheless, due to limited resources, there is an urgent need to enhance both its efficiency and therapeutic efficacy. The integration of Artificial Intelligence (AI) into RT has proven to significantly improve treatment efficiency, especially in time-consuming tasks. This perspective demonstrates how AI enhances the efficiency of target delineation and treatment planning, and introduces the concept of All-in-One RT, which may greatly improve RT efficiency. Furthermore, the concept of Radiotherapy Digital Twins (RDTs) is introduced. By integrating patient-specific data with AI, RDTs enable personalized and precise treatment, as well as the evaluation of therapeutic efficacy. This perspective highlights the transformative impact of AI and digital twin technologies in revolutionizing cancer RT, with the aim of making RT more accessible and effective on a global scale.

Indexed as

artificial intelligencedigital twinsradiotherapy

Identifiers

PMID40838109
PMCPMC12362058

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.