Evidence map›Paper›PMID 40564445›Full record

ArticleBioengineering (Basel, Switzerland)2025

LL-MAROCO: A Large Language Model-Assisted Robotic System for Oral and Craniomaxillofacial Osteotomy.

Lai Jiang, Liangjing Shao, Jinyang Wu, Xiaofeng Xu, Xinrong Chen, Shilei Zhang

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Lai JiangDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
Liangjing ShaoAcademy for Engineering and Technology, Fudan University, Shanghai 200433, China.ORCID 0009-0009-9176-579X
Jinyang WuDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.ORCID 0000-0002-3285-7389
Xiaofeng XuDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
Xinrong ChenAcademy for Engineering and Technology, Fudan University, Shanghai 200433, China.ORCID 0000-0002-4186-0678
Shilei ZhangDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.

Funding

CAMS Innovation Fund for Medical Sciences (CIFMS) 2019-I2M-5-037National Clinical Research Center for Oral Diseases NCRCO202307 and NCRCO202106Natural Science Foundation of Shanghai, Shanghai Action Plan for Science, Technology and Innovation 24ZR1443700Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention 21DZ2271700-1SJTU Trans-med Awards Research 20230104Xuhui District Courtyard Local Cooperation Project 23XHYD-23
6 · The paper itself

Abstract

Oral and craniomaxillofacial bone deformities necessitate treatment through osteotomy. Robot-assisted osteotomy appears promising in oral and craniomaxillofacial surgery, but it lacks sufficient intelligence and comprehensive integration of navigation tracking with surgical planning. This study aims to develop an intelligent surgical robot, based on the large language model ChatGPT-4, to enable autonomous planning for oral and craniomaxillofacial osteotomies. An autonomous surgical planning system driven by ChatGPT-4 was developed. Surgical plans were autonomously generated based on expert-defined prompts and surgical objectives. A deep learning framework was employed to match navigation-generated visual data with textual planning outputs. The generated plans were subsequently converted into executable instructions for robotic surgery. System precision, execution accuracy, and usability were experimentally validated through common osteotomies. An anonymous Likert scale assessed operational efficiency. The proposed system achieved a trajectory planning accuracy of 0.24 mm and an average robotic execution accuracy of 1.46 mm. The completion rates for two representative procedures, Le Fort I osteotomy and genioplasty, were 87% and 92%, respectively. Survey results confirmed process feasibility. The integration of a large language model with surgical robot advances intelligent, precise, and safe oral and craniomaxillofacial osteotomy procedures.

Indexed as

automatic surgery systemlarge language modeloral and craniomaxillofacial surgeryosteotomyrobot-assisted surgery system

Identifiers

PMID40564445
PMCPMC12189646

What OpenQuestion holds

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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.