Evidence map›Paper›PMID 40636729›Full record

ReviewiLIVER2024

Artificial intelligence-based pathological analysis of liver cancer: Current advancements and interpretative strategies.

Guang-Yu Ding, Jie-Yi Shi, Xiao-Dong Wang, Bo Yan, Xi-Yang Liu, Qiang Gao

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iLIVER, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Guang-Yu DingDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Shanghai 200032, China.
Jie-Yi ShiDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Shanghai 200032, China.
Xiao-Dong WangSchool of Computer Science and Technology, Xidian University, Xi'an 710126, China.
Bo YanSchool of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China.
Xi-Yang LiuSchool of Computer Science and Technology, Xidian University, Xi'an 710126, China.
Qiang GaoDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Shanghai 200032, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, significant advances have been achieved in liver cancer management with the development of artificial intelligence (AI). AI-based pathological analysis can extract crucial information from whole slide images to assist clinicians in all aspects from diagnosis to prognosis and molecular profiling. However, AI techniques have a "black box" nature, which means that interpretability is of utmost importance because it is key to ensuring the reliability of the methods and building trust among clinicians for actual clinical implementation. In this paper, we provide an overview of current technical advancements in the AI-based pathological analysis of liver cancer, and delve into the strategies used in recent studies to unravel the "black box" of AI's decision-making process.

Indexed as

Artificial intelligenceInterpretative modelLiver cancerPathology

Identifiers

PMID40636729
PMCPMC12212694

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.