Evidence map›Paper›PMID 42663785›Full record

ArticleJournal of robotic surgery2026

Robot-assisted biopsy a bibliometric analysis of global trends, hotspots, and emerging frontiers (2000-2025).

Yanwen Liu, Shanshan Li, Chenggang Liu

Abstract read
In one paragraph

Article in Journal of robotic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Yanwen Liu *Jiangning District, Nanjing University of Aeronautics and Astronautics, Jiangjun Road No.29, Nanjing, 211106, China.
Shanshan Li *School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, 24 Heping Road, Harbin, 150040, China.
Chenggang LiuSchool of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, 24 Heping Road, Harbin, 150040, China. liuchenggang@hljucm.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robot-assisted biopsy techniques achieve precise minimally invasive sampling of deep-seated and anatomically complex lesions through stable robotic manipulation, intelligent path planning, and multimodal imaging fusion. These techniques have accumulated substantial evidence in urology, neurosurgery, and thoracic intervention. However, the field spans multiple organs and technical directions, and lacks a systematic, macroscopic depiction of its overall knowledge structure, evolutionary trajectory, and emerging frontiers. This study aims to systematically map global research trends, the distribution of core research actors, the evolution of the knowledge structure, and emerging frontier directions in robot-assisted biopsy via bibliometric methods, thereby providing a macro-level evidence map for researchers, clinicians, and policymakers. Using the Web of Science Core Collection as the primary data source, we retrieved literature related to robot-assisted biopsy from 2000 to 2025, limited to English Articles and Reviews; an independent dataset was constructed from PubMed for semantic validation at the MeSH term level. Descriptive statistics, collaboration networks, co-citation networks, keyword co-occurrence, citation burst detection, and dual-map overlay analysis of journals were conducted using Bibliometrix, VOSviewer 1.6.20, CiteSpace 6.4.R1, and Scimago Graphica. A total of 2,336 publications were included, with an average annual growth rate of 16.27%. Research progressed through three phases: a slow start from 2000-2009, steady growth from 2010-2016, and accelerated expansion after 2017; publications since 2017 account for 70.85% of the total. The United States (826 publications, 35.4%) ranked first in both productivity and academic impact; China (380 publications, 16.3%) ranked second in output but had a lower citations-per-paper (14.4). Engineering and clinical medicine journals form a dual-core citation structure, with medical imaging journals positioned as interdisciplinary bridging hubs. Keyword analysis showed biopsy(618 occurrences) and medical robotics(612 occurrences) at the network core; prostate cancer, accuracy, and diagnosis constitute the traditional research mainline, while terms such as diagnostic yield, navigation, and artificial intelligence have become increasingly active in recent years. Citation burst analysis indicates a shift in research focus from early robotic system design (2006-2013) to clinical diagnostic paradigms (post-2017), with persistent citation bursts in recent years for pulmonary robotic bronchoscopy literature. MeSH term analysis of the independent PubMed validation set (1,531 publications) further confirmed main research lines including robotic technology, prostate tumors, image-guided biopsy, and pulmonary robotic intervention. Robot-assisted biopsy is transitioning from validation of technical feasibility toward creation of clinical value. Prostate biopsy represents the most mature clinical translation pathway; robot-assisted bronchoscopy is the one of the most rapidly growing emerging directions. AI-driven autonomous navigation and human-machine collaboration are reshaping the technological landscape of the field. Future efforts should emphasize multicenter comparative studies, standardized outcome measures, and health-economic evaluations to facilitate integration of robot-assisted biopsy into routine clinical practice.

Indexed as

BiopsyRobotic Surgical ProceduresBibliometricsHumansReview Literature as TopicArtificial intelligenceBibliometric analysisProstate cancerRobotic-assisted biopsyRobotic bronchoscopy

Identifiers

PMID42663785
PMCPMC13525022

What Socratic holds

Textmetadata
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.