Evidence map›Paper›PMID 40856916›Full record

ReviewDiscover oncology2025

Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.

Junyan Huang, Yizhen Xiang, Shengqi Gan, Linrong Wu, Jiangyu Yan, Dong Ye, Junjun Zhang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
–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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  12. A multifunctional FeFrontiers in bioengineering and biotechnology · 2026
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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

7 authors.

Junyan HuangDepartment of Radiology, the Second People's Hospital of Lishui, Wenzhou Medical University, Lishui, Zhejiang, China. 2804092069@qq.com.
Yizhen XiangDigital Health Center, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany.
Shengqi GanDepartment of Otorhinolaryngology-Head and Neck Surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China.
Linrong WuDepartment of Otorhinolaryngology-Head and Neck Surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China.
Jiangyu YanDepartment of Otorhinolaryngology-Head and Neck Surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China.
Dong YeDepartment of Otorhinolaryngology-Head and Neck Surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China.
Junjun ZhangDepartment of Trauma Surgery, Yinzhou No.2 Hospital, Ningbo, Zhejiang, China. 604988147@qq.com.

Funding

2024 Ningbo Public Welfare Science and Technology Plan Key Project No. 2024S032Medical and Health Research Project of Zhejiang Provinc 2024KY1481,2025KY1296
6 · The paper itself

Abstract

This narrative review provides a comprehensive and structured overview of recent advances in the application of artificial intelligence (AI) to medical imaging for tumor diagnosis and treatment. By synthesizing evidence from recent literature and clinical reports, we highlight the capabilities, limitations, and translational potential of AI techniques across key imaging modalities such as CT, MRI, and PET. Deep learning (DL) and radiomics have facilitated automated lesion detection, tumour segmentation, and prognostic assessments, improving early cancer detection across various malignancies, including breast, lung, and prostate cancers. AI-driven multi-modal imaging fusion integrates radiomics, genomics, and clinical data, refining precision oncology strategies. Additionally, AI-assisted radiotherapy planning and adaptive dose optimisation have enhanced therapeutic efficacy while minimising toxicity. However, challenges persist regarding data heterogeneity, model generalisability, regulatory constraints, and ethical concerns. The lack of standardised datasets and explainable AI (XAI) frameworks hinders clinical adoption. Future research should focus on improving AI interpretability, fostering multi-centre dataset interoperability, and integrating AI with molecular imaging and real-time clinical decision support. Addressing these challenges will ensure AI's seamless integration into clinical oncology, optimising cancer diagnosis, prognosis, and treatment outcomes.

Indexed as

Artificial intelligenceDeep learningMulti-modal fusionOncologic imagingPrecision oncologyRadiomicsTumour segmentation

Identifiers

PMID40856916
PMCPMC12381339

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.