Evidence map›Paper›PMID 39871340›Full record

ReviewJournal of translational medicine2025

The clinical application of artificial intelligence in cancer precision treatment.

Jinyu Wang, Ziyi Zeng, Zehua Li, Guangyue Liu, Shunhong Zhang, Chenchen Luo, Saidi Hu, Siran Wan, Linyong Zhao

Abstract readReview
In one paragraph

Review in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

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  7. [Current status and future perspectives of precision treatment for locally advanced rectal cancer].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 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

9 authors.

Jinyu WangDepartment of Medical Genetics, West China Second University Hospital, Sichuan University, Chengdu, China.
Ziyi ZengKey Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China.
Zehua LiDepartment of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, China.
Guangyue LiuDepartment of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China.
Shunhong ZhangDepartment of Cardiology, Panzhihua Iron and Steel Group General Hospital, Panzhihua, China.
Chenchen LuoDepartment of Outpatient Chengbei, the Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, China.
Saidi HuDepartment of Stomatology, Yaan people's Hospital, Yaan, China.
Siran WanDepartment of Gynaecology and Obstetrics, Yaan people's Hospital, Yaan, China.
Linyong ZhaoDepartment of General Surgery & Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy / Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China. 153795352@scu.edu.cn.ORCID http://orcid.org/0000-0003-0884-4657

Funding

Innovative Research Group Project of the National Natural Science Foundation of China No.82104212
6 · The paper itself

Abstract

backgroundArtificial intelligence has made significant contributions to oncology through the availability of high-dimensional datasets and advances in computing and deep learning. Cancer precision medicine aims to optimize therapeutic outcomes and reduce side effects for individual cancer patients. However, a comprehensive review describing the impact of artificial intelligence on cancer precision medicine is lacking. OBSERVATIONS: By collecting and integrating large volumes of data and applying it to clinical tasks across various algorithms and models, artificial intelligence plays a significant role in cancer precision medicine. Here, we describe the general principles of artificial intelligence, including machine learning and deep learning. We further summarize the latest developments in artificial intelligence applications in cancer precision medicine. In tumor precision treatment, artificial intelligence plays a crucial role in individualizing both conventional and emerging therapies. In specific fields, including target prediction, targeted drug generation, immunotherapy response prediction, neoantigen prediction, and identification of long non-coding RNA, artificial intelligence offers promising perspectives. Finally, we outline the current challenges and ethical issues in the field.

conclusionsRecent clinical studies demonstrate that artificial intelligence is involved in cancer precision medicine and has the potential to benefit cancer healthcare, particularly by optimizing conventional therapies, emerging targeted therapies, and individual immunotherapies. This review aims to provide valuable resources to clinicians and researchers and encourage further investigation in this field.

Indexed as

Artificial IntelligenceNeoplasmsPrecision MedicineDeep LearningHumansImmunotherapyDeep learningImmunotherapyMachine learningPrecision radiotherapySolid tumorTargeted therapyTumor microenvironment

Identifiers

PMID39871340
PMCPMC11773911

What Socratic 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 Socratic graph.