Evidence map›Paper›PMID 41371995›Full record

ReviewBiomolecules & therapeutics2026

Integrating Robotic Bilateral Axillo-Breast Approach Thyroidectomy with Molecular Diagnostics and Artificial Intelligence in Thyroid Cancer Care.

Qiang Deng, Xiaoping Men, Duo Jin, Yuzhuo Bai

Abstract readReview
In one paragraph

Review in Biomolecules & therapeutics, 2026. 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. Review
  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

4 authors.

Qiang DengDepartment of Breast and Thyroid Surgery, Affiliated Hospital of Changchun University of Chinese Medicine, Jilin, 130000, China.
Xiaoping MenDepartment of Clinical Laboratory, Affiliated Hospital of Changchun University of Chinese Medicine, Jilin130000, China.
Duo JinDepartment of Clinical Laboratory, Affiliated Hospital of Changchun University of Chinese Medicine, Jilin130000, China.
Yuzhuo BaiDepartment of Breast and Thyroid Surgery, Affiliated Hospital of Changchun University of Chinese Medicine, Jilin, 130000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thyroid cancer is one of the most prevalent cancers in the world, accounting for the increased sensitivity of diagnostic assessments, the environment, and extensive imaging. While open thyroidectomy still forms the base surgical treatment of differentiated thyroid carcinoma (DTC), its visible scarring and postoperative morbidity have mandatorily propelled a longitudinal shift to minimally invasive techniques. Robotic thyroidectomy using the bilateral axillo-breast approach (BABA) is an emerging transformative technique that combines robotic precision and visualization with oncologic efficacy and superior cosmetic outcomes to enhance recovery. Molecular diagnostics such as next-generation sequencing (NGS) and microRNA classifiers have transformed preoperative planning. These tools aid in accurate risk stratification, enabling clinicians to determine the course of surgery and avoid overtreatment. Artificial intelligence (AI) enhances precision medicine by improving nodule classification, predicting surgical risks, providing intraoperative navigation guidance, and supporting postoperative histopathological evaluations. Despite these innovations, cost, accessibility, and ethical governance issues highlight persistent challenges. This review consolidates the current state of robotic-assisted surgery for thyroid cancer with molecular profiling and AI. It proposes future aims and strides toward precision surgery that is accessible worldwide.

Indexed as

Artificial intelligenceBilateral axillo-breast approachNext-generation sequencingOncologicThyroidectomy

Identifiers

PMID41371995
PMCPMC12782870

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.