Evidence map›Paper›PMID 40469709›Full record

ReviewCytoJournal2025

Artificial intelligence in cancer pathology: Applications, challenges, and future directions.

Jiule Wang, Teng Wang, Rui Han, Dongmei Shi, Biao Chen

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in CytoJournal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Review
  2. Artificial intelligence-based quantification of epidermal proliferation and apoptosis in human skin.JID innovations : skin science from molecules to population health · 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

5 authors.

Jiule Wang *School of Pharmaceutical Sciences, Shandong University, Jinan, Shandong, China.
Teng Wang *Department of Medical Laboratory, Key Laboratory of Jining high-throughput gene sequencing, Key Laboratory of Jining Prenatal Monitoring and Genetic Disease Research, Key Laboratory of Jining Pathogen Nanobiotic Prevention and Control New Technology Research, Jining No. 1 People's Hospital, Jining, Shandong, China.
Rui HanDepartment of Medical Laboratory, Key Laboratory of Jining high-throughput gene sequencing, Key Laboratory of Jining Prenatal Monitoring and Genetic Disease Research, Key Laboratory of Jining Pathogen Nanobiotic Prevention and Control New Technology Research, Jining No. 1 People's Hospital, Jining, Shandong, China.
Dongmei ShiLaboratory of Medical Mycology, Jining No. 1 People's Hospital, Jining, Shandong, China.
Biao ChenCentral Laboratory, Jining key laboratory for the intelligent diagnosis of emerging infectious diseases, Jining, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The application of artificial intelligence (AI) in cancer pathology has shown significant potential to enhance diagnostic accuracy, streamline workflows, and support precision oncology. This review examines the current applications of AI across various cancer types, including breast, lung, prostate, and colorectal cancer, where AI aids in tissue classification, mutation detection, and prognostic predictions. The key technologies driving these advancements include machine learning, deep learning, and computer vision, which enable automated analysis of histopathological images and multi-modal data integration. Despite these promising developments, challenges persist, including ensuring data privacy, improving model interpretability, and meeting regulatory standards. Furthermore, this review explores future directions in AI-driven cancer pathology, including real-time diagnostics, explainable AI, and global accessibility, emphasizing the importance of collaboration between AI and pathologists. Addressing these challenges and leveraging AI's full potential could lead to a more efficient, equitable, and personalized approach to cancer care.

Indexed as

Artificial intelligenceCancer pathologyDeep learningGlobal accessibilityMachine learning

Identifiers

PMID40469709
PMCPMC12134795

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.