Evidence map›Paper›PMID 32277744›Full record

ReviewCancer communications (London, England)2020

Emerging role of deep learning-based artificial intelligence in tumor pathology.

Yahui Jiang, Meng Yang, Shuhao Wang, Xiangchun Li, Yan Sun

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancer communications (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06081569 (Multimodal Deep Learning for the Diagnosis and Assessment of Alzheimer's Disease), which is not on this map. Cited by 231 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
231citing papers in PubMed, 8 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.

NCT06081569 not yet recruitingnot on this mapstarted 2023, after this paper: background citation

Multimodal Deep Learning for the Diagnosis and Assessment of Alzheimer's Disease

TypeobservationalSponsorFirst Hospital of China Medical UniversityRan2023 to 2026Enrolled300ConditionsAlzheimer Disease, Mild Cognitive ImpairmentArmsgait video, speech video, facial expression video
3 · Its place in the literature

Who cites it

231 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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171 more citing papers are in PubMed but not listed here.

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.

Yahui JiangDepartment of Pathology, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Cancer Institute and Hospital, Tianjin Medical University, Tianjin, 300060, P. R. China.
Meng YangDepartment Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Cancer Institute and Hospital, Tianjin Medical University, Tianjin, 300060, P.R. China.
Shuhao WangInstitute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, P. R. China.
Xiangchun LiDepartment Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Cancer Institute and Hospital, Tianjin Medical University, Tianjin, 300060, P.R. China.
Yan SunDepartment of Pathology, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Cancer Institute and Hospital, Tianjin Medical University, Tianjin, 300060, P. R. China.ORCID 0000-0001-8916-9598

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

Indexed as

Artificial IntelligenceDeep LearningHumansNeoplasmsPrecision Medicineartificial intelligenceartificial intelligence-assisted bioinformatic analysisdeep learningpathologytumor

Identifiers

PMID32277744
PMCPMC7170661

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Registered trials

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.