Evidence map›Paper›PMID 41897565›Full record

ReviewDiagnostics (Basel, Switzerland)2026

Innovations in Robotic-Assisted Bronchoscopy: Current Trends and Future Prospects.

Joshua M Boster, S Michael Goertzen, Brian D Tran, Robert F Browning

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 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

4 authors.

Joshua M BosterDepartment of Medicine, Pulmonary Critical Care Medicine, Interventional Pulmonology, Walter Reed National Military Medical Center, Bethesda, MD 20889, USA.ORCID 0000-0002-0823-5811
S Michael GoertzenDepartment of Pulmonology and Critical Care, Brooke Army Medical Center, Fort Sam Houston, San Antonio, TX 78234, USA.ORCID 0009-0006-3563-0055
Brian D TranDepartment of Pulmonology and Critical Care, University of California San Diego, San Diego, CA 92093, USA.ORCID 0009-0001-2189-0837
Robert F BrowningDepartment of Medicine, Pulmonary Critical Care Medicine, Interventional Pulmonology, Walter Reed National Military Medical Center, Bethesda, MD 20889, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robotic-assisted bronchoscopy (RAB) represents a significant technological advance, providing superior precision, enhanced visualization, and increased maneuverability relative to conventional bronchoscopic methods. This review provides an overview of current research evaluating RAB's diagnostic performance and exploring future prospects. Recent literature demonstrates advantages in navigating difficult-to-reach lung lesions with improved safety profiles compared to transthoracic approaches. Incorporating advanced imaging technologies has enhanced real-time decision-making during procedures, and artificial intelligence applications are emerging. RAB has been rapidly adopted at many high-volume centers based on favorable navigational success and safety data. As the field matures, ongoing prospective studies will further define its role in improving patient outcomes, cost-effectiveness, and optimal integration with lung cancer screening programs. RAB faces ongoing challenges including substantial capital costs, training requirements, and need for standardized protocols. Therapeutic applications show promise and are under active investigation.

Indexed as

artificial intelligencebronchoscopycancercone-beam computed tomographyinterventional pulmonologylung diseasesrobotic-assisted bronchoscopy

Identifiers

PMID41897565
PMCPMC13025912

What OpenQuestion holds

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LicenceCC BY
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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.