Evidence map›Paper›PMID 39035195›Full record

ReviewJournal of the National Cancer Center2023

An overview of artificial intelligence in medical physics and radiation oncology.

Jiali Liu, Haonan Xiao, Jiawei Fan, Weigang Hu, Yong Yang, Peng Dong, Lei Xing, Jing Cai

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of the National Cancer Center, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Uncertainties in outcome modelling in radiation oncology.Physics and imaging in radiation oncology · 2025
    Review
  5. Article
  6. Article
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

8 authors.

Jiali LiuDepartment of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
Haonan XiaoDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Jiawei FanDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Weigang HuDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Yong YangDepartment of Radiation Oncology, Stanford University, CA, USA.
Peng DongDepartment of Radiation Oncology, Stanford University, CA, USA.
Lei XingDepartment of Radiation Oncology, Stanford University, CA, USA.
Jing CaiDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is developing rapidly and has found widespread applications in medicine, especially radiotherapy. This paper provides a brief overview of AI applications in radiotherapy, and highlights the research directions of AI that can potentially make significant impacts and relevant ongoing research works in these directions. Challenging issues related to the clinical applications of AI, such as robustness and interpretability of AI models, are also discussed. The future research directions of AI in the field of medical physics and radiotherapy are highlighted.

Indexed as

Artificial intelligenceMedical physicsRadiotherapy

Identifiers

PMID39035195
PMCPMC11256546

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.