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148-158 

148 

 

 

 

Article 

From robotic arms to AI-assisted: the evolution and 

interdisciplinary integration of robotic surgery 

technology based on bibliometron  
Yuchi Liu, Mohd Wira Mohd Shafiei*  

Centre for Global Sustainability Studies, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 15 April 2025  
Received in revised form 
25 May 2025 
Accepted 07 June 2025 
 
Keywords:  
Robotic surgery, Artificial intelligence, 
Bibliometric analysis, Technology evolution, 
Interdisciplinary research 
 
*Corresponding author 
Email address: 
wira@usm.my 
 
 
DOI: 10.55670/fpll.futech.4.3.14 

A B S T R A C T 
 

With medical technology innovation, robotic surgery has evolved from 
mechanical arm operations to AI-assisted decision-making, promoting deep 
integration of surgical medicine with engineering and computer science. This 
study employed CiteSpace software to conduct a bibliometric analysis of robotic 
surgical technology evolution literature from the Web of Science (2014-2024). 
Analysis of 520 publications revealed explosive growth from <5 annual papers 
(2014-2017) to 177 papers in 2024, representing a 3,540% increase. The 
dataset encompassed 2,968 authors, 1,957 institutions, and 266 journals across 
77 countries/regions. The United States dominated with 191 publications 
(36.73%), followed by China (88, 16.92%) and the United Kingdom (71, 
13.65%). The University of London emerged as the most productive institution 
(28 publications). Keyword burst analysis identified "artificial intelligence" 
(2019-2024) and "deep learning methods" (2022-2024) as dominant emerging 
themes. Computer science categories comprised >10% of publications, 
demonstrating strong interdisciplinary integration centered on surgery 
(31.54%) and biomedical engineering (12.31%). The field demonstrated clear 
evolution from basic instrument innovation to AI-driven, multi-disciplinary 
collaborative intelligent surgical systems, with Italy (centrality 0.18) and 
France (0.16) serving as critical knowledge brokers despite moderate 
publication volumes. 

1. Introduction 

Despite significant technological advances in robotic 

surgery and artificial intelligence, the research landscape 

lacks comprehensive quantitative analysis of their integration 

patterns and evolution trajectories. Current literature 

primarily focuses on isolated technical developments without 

providing systematic evidence of interdisciplinary 

collaboration trends, research hotspots, and knowledge 

diffusion mechanisms. This gap limits our understanding of 

how this field has evolved and where future research 

directions are heading. A bibliometric analysis is needed to 

objectively map the research landscape, identify influential 

contributors, and reveal emerging trends in the integration of 

robotic surgery with AI technologies. Since their emergence 

in the late 1980s, surgical robots have become increasingly 

fundamental to modern medical procedures [1,2]. In recent 

years, with the speedy improvement of cloud computing, big 

data analysis, artificial intelligence, and precision medicine, 

robotic surgical technology has developed from an important 

auxiliary tool in surgical operations to a smart surgical robot 

with autonomous perception, intelligent decision-making, 

and personalized operation capabilities [3]. This change no 

longer makes surgical operations increasingly specific and 

minimally invasive, but also promotes the mutual integration 

of surgical operations, engineering, and computer science [4]. 

Studies display that robotic surgical systems have 

substantially enhanced the safety and accessibility of complex 

surgical procedures. However, their limited ability to process 

complex information and make surgical decisions has not 

been effectively addressed [5]. However, most of the current 

literature focuses on a single technical field, such as human-

machine assistance and the application of AI algorithms, 

lacking quantitative analysis of technological evolution [6].  

Therefore, this study employs bibliometric analysis using the 

Web of Science Core Collection as the primary data source to 

systematically examine research literature on robotic surgery 

and artificial intelligence integration from 2014 to 2024. 

Using CiteSpace software, we constructed visual knowledge 

maps and applied co-occurrence analysis and cluster analysis 

to reveal research hotspots and evolutionary trends. 

Open Access Journal 

 

 

ISSN 2832-0379 

August 2025| Volume 04 | Issue 03 | Pages 148-158 

https://doi.org/10.55670/fpll.futech.4.3.14 

 

 

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Open Access Journal 

mailto:wira@usm.my
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Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

149 

 

 

This study aims to achieve four specific objectives: (a) 
map and analyze the characteristics of global collaboration 
networks in this interdisciplinary field; (b) identify influential 
authors and key research themes through highly cited 
publications and keyword frequency analysis; (c) trace the 
evolution paths and structural changes of major research 
categories over the past decade; (d) detect emerging research 
frontiers and predict future development directions in 
robotic surgery-AI integration. 

2. Data acquisition and methods 

2.1 Data collection 
The data used in this study are all derived from the Web 

of Science Core Collection (WoSCC) database, which is an 
online academic citation index system developed by Thomson 
Reuters, and its literature indexing began in 1900 [7]. The 
content of WoSCC covers multiple disciplines, including 
natural sciences, social sciences, humanities, and arts, and 
includes over 12,000 high-impact journals, more than 
150,000 conference papers, and a large amount of open 
access resources [8]. It has rich existing and historical data 
and supports in-depth interdisciplinary research. The 
literature data for this article were retrieved through a 
literature search in the WoSCC database. The search strategy 
was developed through a systematic approach to ensure 
comprehensive coverage. Keywords related to robotic 
surgery were selected based on: (1) a preliminary review of 
seminal papers in the field, (2) consultation with domain 
experts, and (3) analysis of Medical Subject Headings (MeSH) 
terms. The final keyword set included "robotic surgery" OR 
"robot-assisted surgery" OR "surgical robot" OR "da Vinci 
surgery" OR "minimally invasive robotic surgery" OR 
"computer-assisted surgery". To capture interdisciplinary 
integration, we included technology-related terms: "artificial 
intelligence" OR "AI" OR "machine learning" OR "deep 
learning" OR "computer vision" OR "neural networks" OR 
"interdisciplinary" OR "cross-disciplinary" OR "human-robot 
interaction". Alternative terms such as "laparoscopic robot", 
"surgical automation", and "intelligent surgery" were tested 
in pilot searches but yielded minimal additional relevant 
results. The time range (January 2014 to December 2024) 
was justified as it captures the period following the 
widespread adoption of da Vinci systems (post-2013) and the 
emergence of AI integration in surgical robotics, while 
including the most recent developments in the fieldA total of 
520 articles were selected. To ensure data quality, the 
following procedures were applied: (1) author name 
standardization using CiteSpace's disambiguation function 
with manual verification for high-frequency authors; (2) 
institutional affiliation unification (e.g., consolidating 
department variations under parent institutions); (3) 
exclusion of non-peer-reviewed materials (editorials, letters, 

conference abstracts without full papers); (4) manual 
validation of a 10% random sample to verify relevance and 
metadata accuracy. While our search strategy aimed for 
comprehensiveness, certain limitations should be 
acknowledged. The focus on English-language publications 
may have excluded relevant research published in other 
languages. Additionally, conference proceedings and gray 
literature were not systematically included, which might have 
resulted in missing some cutting-edge developments that 
have not yet been published in peer-reviewed journals. 
Moreover, WoSCC has known indexing biases toward 
Western and English-language publications, potentially 
underrepresenting research contributions from non-English-
speaking regions and limiting the global perspective of 
findings. 

2.2 Bibliometric analysis and tools 
CiteSpace is a visualization tool specifically designed for 

bibliometric analysis. It can be used to help answer questions 
related to knowledge domains and to study the relationships 
between academic references, authors, and journals [9]. This 
software can generate co-occurrence network diagrams of 
keywords, authors, countries/regions, and institutions under 
a specific research topic [10]. In this study, the parameters of 
CiteSpace were set as a time span from 2014 to 2024, with a 
time slice interval of 1 year. Based on different analysis 
requirements, this paper extracts literature data related to 
robotic surgery and its interdisciplinary technological 
integration from the WoSCC database, and conducts analysis 
from multiple perspectives, including keyword clustering, 
citation emergence detection, distribution of countries and 
regions, institution cooperation networks, and evolution of 
research topic categories. 

3. Results and discussion 

This paper conducts a two-stage study on the evolution 
of robotic surgery technology: Firstly, it performs descriptive 
statistics to summarize the development overview of this 
field in various aspects, including the distribution 
characteristics of countries/regions, research institutions, 
authors, co-cited authors, co-cited journals, and keywords; 
Secondly, using the bibliometric and visualization analysis 
tool CiteSpace, it deeply analyzes the evolution path of 
research topics, revealing the main features and development 
trends of the evolution of robotic surgery technology. 

3.1 Descriptive analysis 
3.1.1 Distribution of published literature on the 

evolution of robotic surgery technology 
The number of documents reflects to a certain extent, 

the research level and development speed of the relevant 
field. As shown in Figure 1, the evolution of robotic surgery 
technology has shown different fluctuations across different 
years. The overall trend can be divided into three stages. The 
first stage is the initial exploration stage (2014-2017), during 
which the number of related literature was extremely small 
each year, with an average of less than 5 papers published 
annually, indicating that this research direction is still in its 
infancy and has relatively low academic attention. Secondly, 
the mid-term development stage (2018-2021): The number 
of literature in this stage has been increasing year by year, 
reaching 42 in 2020 and 69 in 2021, indicating that this field 
has gradually attracted attention, and the research 
enthusiasm for the evolution of robotic surgery technology 
has steadily risen. Finally, there is the explosive growth stage 
(2022-2024). As shown in Figure 1, research enthusiasm in 
this field has increased significantly since 2022. In 2022, there 

Abbreviations 

AI                                     Artificial Intelligence 

CiteSpace                      Citation Space 

CV                                    Computer Vision 

DL                                    Deep Learning 

DOI                                  Digital Object Identifier 

HRI                                  Human-Robot Interaction 

MeSH                              Medical Subject Headings 

ML                                   Machine Learning 

WoS                                Web of Science   

WoSCC                           Web of Science Core Collection 

 

 

 

 

 

 

 

 

 

 



Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

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were 92 related papers, which rose to 177 in 2024, accounting 
for 34.23% of the total number of published papers. This 
publication surge is corroborated by keyword burst analysis 
(Figure 12), which shows the emergence of "deep learning 
methods" (2022-2024), "computer vision" (2022-2024), and 
"artificial intelligence" (2019-2024) as high-intensity burst 
terms. Additionally, the shift from early keywords like 
"laparoscopic surgery" (burst 2021-2022) to AI-focused 
terms supports the transition from traditional surgical 
robotics to intelligent surgical systems. 

  

Figure 1. Annual distribution of literature related to the evolution of 
Robotic Surgical Techniques from 2014 to 2024 

In the research field, a total of 520 articles were collected for 
analysis, revealing that research on the evolution of robotic 
surgery technology exhibits the characteristics of "clinical 
demand leading, engineering technology driving, and deep 
cross-integration of multiple disciplines." Its development 
trajectory has gradually evolved from early surgical clinical 
exploration to an intelligent development stage supported by 
cutting-edge technologies such as artificial intelligence and 
5G communication, fully demonstrating the interdisciplinary 
collaborative paradigm of modern medical technological 
innovation. Based on the statistics of the Web of Science 
classification, a total of 80 subject categories were retrieved, 
covering medicine, engineering, computer science, and other 
fields. This interdisciplinary integration is evidenced by: (1) 
Computer Science categories accounting for >10% of 
publications (Table 1), compared to <5% in traditional 
surgical fields; (2) Co-occurrence analysis showing "artificial 
intelligence" and "machine learning" as central nodes with 
high connectivity (Figure 10); (3) The emergence of hybrid 
research clusters combining surgical and computational 
themes (Figure 11). As shown in Table 1, the top 15 subject 
categories collectively contributed over 70% of the literature, 
with a concentrated distribution in core technologies and 
application fields such as surgical procedures (31.54%), 
engineering biomedical (12.31%), and robotics (9.62%). 
Among them, Surgery ranked first, reflecting the medical 
dominance of robotic surgery research; while the high 
proportion of technical disciplines such as biomedical 
engineering and electrical and electronic engineering 
indicates that AI and engineering technologies are the key 
supports for the progress of this field. 

In addition, clinical sub-sectors such as medicine general 
internal, oncology, urology, and general internal medicine 
also occupy significant shares, demonstrating the practical 
application value of robotic surgery in various medical 
departments. Computer science-related categories (such as 
artificial intelligence, information systems, and 
interdisciplinary applications) accounted for more than 10%, 

further indicating that the integration trend of AI in the 
medical field is increasingly strengthening. 

Table 1. Top 15 Web of Science categories 

 

3.1.2 Country and institution distribution 
By analyzing the statistics of the countries/regions 

where the literature was published, it was found that a total 
of 77 countries/regions conducted research related to the 
evolution of robotic surgery techniques. As shown in Table 2, 
the United States ranked first with 191 papers, accounting for 
36.73%, indicating that it holds a core position in the research 
on the evolution of robotic surgery techniques. China (88 , 
16.92%), the United Kingdom (71, 13.65%), Italy (60, 
11.54%), and Germany (40, 7.69%) followed. Figures 2 and 
Figure 3 are visual maps of the countries/regions and 
institutional collaboration networks in the field of robot 
surgery technology evolution in the Web of Science database. 
It is clearly observable that there is a highly interconnected 
research network. Among them, "centrality" is an important 
indicator for measuring the importance of a node in the 
network, reflecting its pivotal role in academic cooperation 
[11]. Values range from 0-1, where ≥0.10 indicates high 
influence: 0.01-0.09 (moderate), 0.10-0.19 (high), ≥0.20 
(exceptional hub status). Centrality should be interpreted 
alongside publication volume, as high centrality with low 
output may indicate strategic rather than sustained research 
leadership. The data shows that the United States, with 191 
publications and a centrality of 0.32, holds a core position in 
global research on robotic surgery technology. Meanwhile, 
Italy (0.18) and France (0.16), although having lower 
publication volumes (60 and 25, respectively), have a 
centrality higher than that of China (0.03) and the United 
Kingdom (0.13), ranking second and third, respectively. This 
suggests Italy and France serve as critical knowledge brokers, 
facilitating research exchange between different regional 
clusters despite moderate research output.  

 

Rank Category Count 
Percentage 
(%) 

1 Surgery 164 31.538 

2 Engineering Biomedical 64 12.308 

3 Robotics 50 9.615 

4 
Engineering Electrical 
Electronic 

37 7.115 

5 Medicine General Internal 32 6.154 

6 Oncology 32 6.154 

7 Urology Nephrology 32 6.154 

8 
Radiology, Nuclear 
Medicine, Medical Imaging 

31 5.962 

9 
Computer Science Artificial 
Intelligence 

20 3.846 

10 
Computer Science 
Interdisciplinary 
Applications 

18 3.462 

11 
Automation Control 
Systems 

17 3.269 

12 Orthopedics 16 3.077 

13 
Computer Science 
Information Systems 

14 2.692 

14 
Instruments 
Instrumentation 

14 2.692 

15 
Health Care Sciences 
Services 

12 2.308 



Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

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Figure 2. Collaboration network map of countries/regions 

In contrast, China's low centrality (0.03), despite high 
output (88 papers), indicates more isolated or regionally-
focused research networks. From the institutional 
perspective, the University of London ranked first with 28 
publications, demonstrating a stable research output. Its 
centrality of 0.11 indicates a strong academic influence. 
Meanwhile, Johns Hopkins University, although having the 
same number of publications (19) as Imperial College 
London, has a centrality of 0.15, the highest among all 
institutions. This shows that it plays a crucial hub role in the 
global academic cooperation network in this field and has 
obvious knowledge-dissemination and cooperation 
organization capabilities. From 2014 to 2024, a total of 520 
research papers on the evolution direction of robotic surgery 
technology were collected from the Web of Science Core 
Collection. The initial search yielded 547 records.  

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Figure 3. Collaboration network map of institutions 

After manual screening for relevance and applying 
inclusion/exclusion criteria (English language publications, 
peer-reviewed articles, and direct relevance to robotic 
surgery technology evolution), 27 articles were excluded, 
resulting in 520 articles. Subsequently, the CiteSpace "delete 
duplicates" function was applied to identify potential 
duplicates based on DOI, title, and author matching. No 
additional duplicates were detected at this stage, confirming 
that the Web of Science database had already eliminated most 
duplicates during the initial search process. Therefore, the 
final dataset consisted of 520 unique articles for bibliometric 
analysis. Using the visualization trimming parameters, a 
country distribution map was generated. As shown in Figure 
2, after running the software, an analysis network consisting 
of 77 nodes and 432 links emerged, with a network density of 
0.1476. 
 

Table 2. The top 10 countries and institutions contributing 

Rank Count Centrality Country Count Centrality Institution 

1 191 0.32 USA 28 0.11 University of London 

2 88 0.03 PEOPLES R 

CHINA 

19 0.1 Imperial College London 

3 71 0.13 ENGLAND 19 0.15 Johns Hopkins University 

4 60 0.18 ITALY 18 0.02 University College London 

5 40 0.13 GERMANY 16 0.03 Harvard University 

6 28 0.11 INDIA 15 0.02 University of California 

System 

7 28 0 SOUTH KOREA 15 0.03 Harvard University Medical 

Affiliates 

8 25 0.16 FRANCE 14 0.01 King's College London 

9 23 0.02 CANADA 10 0.05 Chinese Academy of 

Sciences 

10 22 0.05 JAPAN 10 0 Roswell Park 

Comprehensive Cancer 

Center 

 



Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

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As shown in Figure 3, this research field involves a total 
of 263 nodes, forming 696 cooperative links, with a network 
density of 0.0202. In this visualized network, each node 
represents an independent publishing institution, and its size 
is proportional to the number of publications of the 
institution. From the figure, it can be seen that although this 
field has formed a certain scale of institutional cooperation 
network, the overall connection density is relatively low, and 
the core institutions play an important regulatory role in the 
flow of knowledge. Among them, the University of London 
ranked first with 28 articles, distinguished by its pioneering 
work in AI-assisted surgical frameworks and ethical 
governance. Their most cited contribution (203 citations) 
established foundational principles for autonomous robotic 
surgery regulation, while their human-computer interaction 
research (170 citations) advanced deep learning applications 
in gesture recognition and multi-sensor fusion. This 
institution's leadership stems from its unique 
interdisciplinary approach, combining legal, technical, and 
clinical expertise. The most cited document among them 
received a total of 203 citations. It focused on the 
development paths of artificial intelligence (AI) and 
autonomous robotic surgeries within the legal, regulatory, 
and ethical frameworks, proposing that surgical robots can 
learn and perform routine operational tasks under the 
supervision of human surgeons. At the same time, another 
paper from this institution, which was cited 170 times, 
focused on human-computer interaction and remote 
operation technologies in the field of surgical robots, 
particularly in multi-sensor fusion and gesture recognition 
methods based on deep learning. Through these highly 
influential achievements, it can be seen that the University of 
London is at the forefront of the international community in 
promoting the multi-dimensional development of robotic 
surgery technology, especially in terms of technological 
innovation and institutional norms. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3.1.3 Distribution of authors and co-cited authors in the 
study on the evolution of robotic surgery 
technology 
As shown in Figure 4, after adopting the 

Pathfinder/Binary Tree Network algorithm and setting 
pruning parameters, this study constructed an author 
collaboration network. This network consists of 312 nodes 
and 460 connection edges, with a network density of 0.0095. 
In the field of research on the development of robotic surgery 
technology, a total of 2,968 researchers have participated.  

Table 3 presents the information of the top 10 authors in 
terms of the number of published papers, based on the 
number of publications and network centrality indicators, the 
most influential scholars are Stoyanov, Danail (7), Dasgupta, 
Prokar (6), Demomi, Elena (5), and Amparore, Daniele (5). 
Stoyanov, Danail's leadership (7 publications) centers on 
computer vision applications in surgical robotics, particularly 
real-time instrument tracking and surgical workflow analysis. 
Dasgupta, Prokar (6 publications) focuses on clinical 
validation of robotic systems in urological procedures, 
bridging the gap between technological innovation and 
clinical practice. Their complementary expertise—technical 
development and clinical validation—exemplifies the 
interdisciplinary collaboration driving this field. As shown in 
Figure 5, this study constructed a co-occurrence network 
graph of cited authors based on the evolution of robotic 
surgery technology. The network, consisting of 252 nodes and 
439 connections, has a network density of 0.0139. To enhance 
the focus and representativeness of visualization, node 
selection adopts the g-index (k=10) as the pruning criterion. 
This parameter setting has been proven by multiple 
bibliometric studies to effectively balance information 
coverage and network simplicity. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

               Table 3. The top 10 authors and co-cited authors 

Rank Count Centrality Author Count Centrality Co-Cited Author 

1 7 0 Stoyanov, Danail 143 0.25 [ANONYMOUS] 

2 6 0 Dasgupta, Prokar 57 0.15 HUNG AJ 

3 5 0 De momi, Elena 47 0.06 HASHIMOTO DA 

4 5 0 Amparore, Daniele 37 0.12 SHADEMAN A 

5 4 0 Hung, Andrew J 36 0.09 HE KM 

6 4 0 Porpiglia, Francesco 35 0.05 RONNEBERGER O 

7 4 0 Shafiei, Somayeh B 33 0.21 ESTEVA A 

8 4 0 Ma, Runzhuo 29 0.57 AHMIDI N 

9 3 0 Ahn, Hanjong 29 0.03 YANG GZ 

10 3 0 
Kuchenbecker, 

Katherine J 
28 0.03 CHEN J 

 



Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

153 

 

 
Figure 4. Collaboration network map of authors 

 

 

Figure 5. Co-citation network map of cited authors 

The data in Table 3 shows that the most frequently cited 
one is [ANONYMOUS] (143), and its centrality is also ranked 
first. The document with the highest citation count is the 
review article published by Zhu et al. [21] in Nature Reviews 
Materials in 2021, which has been cited 211 times. This paper 
systematically elaborates on the cutting-edge progress of 
artificial intelligence-assisted 3D printing manufacturing 
technology in the field of multifunctional materials, especially 
the application prospects in personalized wearable devices, 
intelligent implants, and integration with surgical robots. 
Secondly, the paper that has been cited more than 203 times 
is the one published by O'Sullivan et al. [15], titled "Legal, 
Regulatory, and Ethical Frameworks for the Development of 
Standards in Artificial Intelligence and Autonomous Robotic 
Surgery". This article explores the legal, regulatory and 
ethical challenges faced by artificial intelligence and 
autonomous surgical robots during their development, and 
has significant theoretical and practical value for the 
construction of the medical AI governance system. 

3.1.4 Distribution of cited journals in the study on the 
evolution of robotic surgery technology 
As shown in Figure 6, the co-citation network 

constructed based on the field of robotic surgery technology 
in this study exhibits significant structural characteristics. 
This network consists of 275 nodes and 434 connection 
edges, with a network density of 0.115. Through the analysis 
of 266 core journals, Table 4 lists the 15 journals with the 
highest citation frequency and their centrality indicators. 
Among them, SURG ENDOSC ranked first with 228 citations 

(centrality 0.2), followed by INT J MED ROBOT COMP (187), 
ANN SURG (186), INT J COMPUT ASS RAD (174), and LECT 
NOTES COMPUT SC (143). It is worth noting that although 
LECT NOTES COMPUT SC ranked fifth in citation frequency, 
its centrality value reached 0.32, indicating the strongest 
network influence. These data results clearly reveal the core 
knowledge sources and dissemination paths in this research 
field. 
 

Figure 6. Network map of co-occurring journals 

Through the analysis of 266 core journals, Table 4 lists 
the 15 most frequently cited journals and their centrality 
indicators. Among them, SURG ENDOSC ranks first with 228 
citations ( 0.2), followed by INT J MED ROBOT COMP (187 ), 
ANN SURG (186), INT J COMPUT ASS RAD (174), and LECT 
NOTES COMPUT SC (143). It is worth noting that although 
LECT NOTES COMPUT SC ranks fifth in citation frequency, its 
centrality value reaches 0.32, indicating the strongest 
network influence. These data results clearly reveal the core 
knowledge sources and dissemination paths in this research 
field. 

Table 4. The top 15 cited journals and the importance index 
(centrality value) 

 

Rank count Centrality Cited Journals 

1 228 0.2 SURG ENDOSC 

2 187 0.31 INT J MED ROBOT COMP 

3 186 0.06 ANN SURG 

4 174 0.02 INT J COMPUT ASS RAD 

5 143 0.32 LECT NOTES COMPUT SC 

6 137 0 J ROBOT SURG 

7 130 0.1 SCI REP-UK 

8 129 0.14 IEEE T BIO-MED ENG 

9 107 0.26 IEEE INT CONF ROBOT 

10 106 0 MED IMAGE ANAL 

11 106 0.04 PLOS ONE 

12 104 0.12 PROC CVPR IEEE 

13 103 0.42 BJU INT 

14 100 0 ARXIV 

15 100 0.08 J UROLOGY 



Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

154 

 

3.2 In-depth analysis 
3.2.1 Distribution of category analysis in the evolution 

research of robotic surgery technology 
Articles in the Internet of Technology (WoS) are 

commonly classified into one or more issue classes  [12]. This 
study covers a total of 80 categories. As shown in Table 5, the 
"SURGERY" category has 164 documents and a centrality of 
0.48, ranking first, indicating the considerable attention and 
central role of this technology within the surgical area. 
Following closely is the "ENGINEERING, BIOMEDICAL" 
category (64, 0.43) and the "ROBOTICS" category (50, 0.02), 
demonstrating the importance of the robotic surgery era now 
not only in scientific applications but additionally as a 
research hotspot in the fields of biomedical engineering and 
robotics. At the same time, we can also observe that 
categories such as "ENGINEERING, ELECTRICAL & 
ELECTRONIC" (37, centrality 0.29), "COMPUTER SCIENCE, 
ARTIFICIAL INTELLIGENCE" (20, centrality 0.05), and 
"AUTOMATION & CONTROL SYSTEMS" also account for a 
certain proportion, reflecting the integration and application 
trends of cutting-edge technologies such as artificial 
intelligence and automation control in this field. 

Table 5. Top 10 research categories in the evolution of robotic 
surgery technology 

 

The citation burst intensity analysis of the disciplinary 
classes associated with the evolution of robotic surgical 
treatment era indicates, as depicted in determine 7, that the 
improvement of this discipline exhibits wonderful traits of 
interdisciplinary integration. "RADIOLOGY, NUCLEAR 
MEDICINE & MEDICAL IMAGING" (2016 - 2020, 4.06) and 
"ENGINEERING, BIOMEDICAL" (2016 - 2018, 3.47) rank at 
the top, indicating that those disciplinary categories have 
always been at the forefront of research in key technical 
factors consisting of image navigation, intraoperative 
imaging, and system integration in robotic surgery. 

Additionally, "MEDICINE, GENERAL & INTERNAL" began to 
experience a rapid burst in 2023 (2023 - 2024, 3.3), 
suggesting that as robotic-assisted surgery gradually 
becomes more widespread in the general medical system, its 
research focus has shifted from engineering development to 
clinical evaluation and wide medical adaptation. 

In terms of the distribution of time, the years when the 
outbreaks began are concentrated from 2016 to 2020. During 
this period, there was a large-scale application 
transformation of technologies such as artificial intelligence, 
imaging technology, and remote operating systems in robotic 
surgery. For instance, fields like "MEDICAL INFORMATICS" 
and "TELECOMMUNICATIONS" experienced a synchronous 
outbreak in 2019, indicating that information processing and 
remote control systems are the core technical supports 
driving the intelligence and networking of robotic surgery. 

At the same time, it is worth noting that the categories 
that experienced outbreaks later, such as "PHYSICS, 
APPLIED" (2021-2022), "HEALTH POLICY & SERVICES" 
(2022), and "MEDICINE, GENERAL & INTERNAL" (2023-
2024), represent research hotspots that have gradually 
expanded from technology implementation to application 
optimization, policy evaluation, and clinical efficacy. This 
"evolution from the source of technology to its final 
implementation" trend reflects that the research on robotic 
surgery is gradually maturing and has a profound impact in 
multiple fields. 

3.2.2 Distribution of commonly cited literature in the 
evolution study of robotic surgery technology 
Table 6 presents the top 15 research papers with the 

highest citation frequency in the field of robotic surgery 
technology during the period from 2014 to 2024, along with 
their key indicators. From the data, it can be seen that the 
paper published by Hashimoto et al. [9] in "ANNALS OF 
SURGERY" ranked first with 24 citations. This paper 
comprehensively summarized the key technologies of 
artificial intelligence in surgical operations and their potential 
applications, emphasizing the core role of surgeons in the 
process of promoting the clinical transformation of AI, and 
providing an important theoretical basis and practical 
guidance for subsequent research in this field. Meanwhile, the 
paper published by Esteva et al. [8] in "Nature" (2017), 
although it was cited only 18 times and ranked fifth, 
demonstrated the centrality index (0.05). Esteva et al. [8] 
focused on the research of skin cancer classification using 
deep convolutional neural networks. It was the first to verify 
the diagnostic ability of artificial intelligence in medical image 
diagnosis at the level of dermatologists on a large-scale image 
dataset. Due to its bridging role in promoting the 
empowerment of artificial intelligence in precision medicine, 
it occupied a key intermediary position in the academic 
network. As shown in Figure 8, this study obtained the co-
citation graph of the cited literature in the evolution research 
of robotic surgery techniques by setting the pruning 
parameter Pathfinder. This network consists of 481 nodes 
and 987 connections, with a network density of 0.0085. This 
indicates that the co-citation relationship is relatively sparse, 
but several significant clustering centers have been formed. 
The color of the nodes represents the time when the cited 
literature first appeared, ranging from purple (2014) to red 
(2025), reflecting the chronological development of the field. 
Larger nodes, such as Funke I (2019), Hashimoto DA [9], 
O'Sullivan S [15], Topol EJ (2019), etc., indicate that they have 
a high co-citation frequency and academic influence in this 
research field. 

Rank Count Centrality Categories 

1 164 0.48 SURGERY 

2 64 0.43 ENGINEERING, BIOMEDICAL 

3 50 0.02 ROBOTICS 

4 37 0.29 
ENGINEERING, ELECTRICAL & 
ELECTRONIC 

5 32 0.35 
MEDICINE, GENERAL & 
INTERNAL 

6 32 0.09 ONCOLOGY 

7 32 0.05 UROLOGY & NEPHROLOGY 

8 31 0.21 
RADIOLOGY, NUCLEAR 
MEDICINE & MEDICAL 
IMAGING 

9 20 0.05 
COMPUTER SCIENCE, 
ARTIFICIAL INTELLIGENCE 

10 18 0.31 
COMPUTER SCIENCE, 
INTERDISCIPLINARY 
APPLICATIONS 



Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

155 

 

 
Figure7. Category burst detection based on WoS data 

 

Figure 8. Co-citation network map of cited references 

As shown in Figure 9, through the citation prominence 
analysis function of CiteSpace, the top 15 highly prominent 
references in the field of robot surgery technology evolution 
from 2014 to 2024 were identified. These papers saw a 
significant increase in citation volume during the specific 
period, indicating their core driving role in related research 
topics. Rusk [17] ranked first with a prominence strength of 
6.33, indicating its significance in algorithm methods and 
basic model research. Secondly, Shademan et al. [18] research 
on the autonomous robotic surgical system also 
demonstrated a relatively high emergence intensity (5.76), 
with the emergence period spanning from 2019 to 2021.  

 

 

 

 

 

 

 

 

 

 

 
Figure 9. The top 15 co-cited references with the strongest citation 
burst 

Table 6. Top 15 most-cited references from 2014 to 2024 

 

 

Rank count Centrality Year Cited References 

1 24 0.08 2018 

Hashimoto DA, 2018, 
ANN SURG, V268, P70, 
DOI 
10.1097/SLA.0000000
000002693 

2 23 0.08 2019 

OSullivan S, 2019, INT 
J MED ROBOT COMP, 
V15, P0, DOI 
10.1002/rcs.1968 

3 20 0.01 2022 

Saeidi H, 2022, SCI 
ROBOT, V7, P0, DOI 
10.1126/scirobotics.a
bj2908 

4 19 0.05 2019 

Funke I, 2019, INT J 
COMPUT ASS RAD, 
V14, P1217, DOI 
10.1007/s11548-019-
01995-1 

5 18 0.05 2017 

Esteva A, 2017, 
NATURE, V542, P115, 
DOI 
10.1038/nature21056 

6 17 0.03 2017 

Twinanda AP, 2017, 
IEEE T MED IMAGING, 
V36, P86, DOI 
10.1109/TMI.2016.25
93957 

7 17 0.06 2021 

Moglia A, 2021, INT J 
SURG, V95, P0, DOI 
10.1016/j.ijsu.2021.10
6151 

8 16 0.01 2019 

Topol EJ, 2019, NAT 
MED, V25, P44, DOI 
10.1038/s41591-018-
0300-7 

9 16 0.05 2019 
Hung AJ, 2019, BJU 
INT, V124, P487, DOI 
10.1111/bju.14735 

10 16 0.05 2016 

Rusk N, 2016, NAT 
METHODS, V13, P35, 
DOI 
10.1038/nmeth.3707 

11 16 0.03 2019 

Panesar S, 2019, ANN 
SURG, V270, P223, 
DOI 
10.1097/SLA.0000000
000003262 

12 15 0.03 2016 

Shademan A, 2016, SCI 
TRANSL MED, V8, P0, 
DOI 
10.1126/scitranslmed.
aad9398 

13 13 0.07 2018 

Wang ZH, 2018, INT J 
COMPUT ASS RAD, 
V13, P1959, DOI 
10.1007/s11548-018-
1860-1 

14 13 0.08 2020 
Lee D, 2020, J CLIN 
MED, V9, P0, DOI 
10.3390/jcm9061964 

15 13 0 2020 

Sheetz KH, 2020, 
JAMA NETW OPEN, 
V3, P0, DOI 
10.1001/jamanetwork
open.2019.18911 



Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

156 

 

The research focus was on constructing and verifying the 
application capability of the "Supervised Autonomous Robot 
System (STAR)" in soft tissue suturing surgeries. This 
research outcome represents a key breakthrough in the 
transformation of robotic surgical technology from "passive 
assistance" to "active execution", marking that surgical robots 
are entering a new stage of intelligence and autonomy. 
Furthermore, as can be seen from the figure, the recently 
emerged literature, such as Hashimoto et al. [8], Mascagni et 
al. [13], and Sheetz et al. [19], has attracted widespread 
attention from the academic community in the years 2022-
2024. It is evident that the focus on robotic surgery in aspects 
such as clinical integration, safety standardization, and 
application efficiency assessment is gradually becoming an 
emerging research hotspot. The research emphasis is shifting 
from technological breakthroughs to clinical transformation 
and system optimization. 

3.2.3 Keyword analysis 
Keywords, as the key elements that reveal the core 

content of a paper, can accurately reflect the core elements 
and main research directions of the research topic [13]. By 
quantitatively studying the frequency distribution of 
keywords, the research hotspots and development trends in 
a specific academic field can be objectively presented [9]. This 
study utilized the keyword co-occurrence analysis function of 
the CiteSpace software to visually process the keyword data 
on the development of robotic surgery technology research in 
the Web of Science database from 2014 to 2024. Finally, the 
high-frequency keyword statistics results are shown in Table 
7. 

Table 7. Top 15 keywords in terms of citation counts and centrality 

 

Based on the keyword co-occurrence analysis results of 
CiteSpace, as shown in Table 7, a total of 15 keywords with 
high frequency of occurrence in the field of robot surgery 
technology evolution research were extracted, reflecting the 
research hotspots and evolution trends of this field. Among 
them, "robotic surgery" (179) and "artificial intelligence" 
(170 times) ranked as the top two, indicating that the deep 
integration of robotic surgery and artificial intelligence has 
become a core issue in recent years. High centrality keywords, 
such as "machine learning" (0.17), "augmented reality" 
(0.14), and "validation" (0.11), played a key bridging role in 
interdisciplinary research and method innovation. Overall, 
the research direction is gradually expanding from the 
application of a single technology to artificial intelligence-
driven surgical assistance systems, intraoperative 
performance evaluation, and human-machine collaboration 
strategies, presenting an evolving trend of technological 
intelligence and clinical integration. As shown in Figure 10, it 
can be seen from the figure that keywords such as 
"augmented reality", "big data", and "surgical robotics" are 
located in the core area of the network, indicating their high 
attention and connectivity in this field, and forming the 
central themes of multiple research clusters. At the same time, 
keywords such as "machine learning", "design", 
"complications", and "manipulation" are distributed around 
the network, reflecting the diversity and refinement of 
research directions such as technical methods, intraoperative 
interaction, and postoperative evaluation. Particularly 
noteworthy is that keywords related to diseases such as 
"human papillomavirus" have also been included in the 
network, indicating that the application research of robotic 
surgery in specific disease treatment is expanding, and 
demonstrating the deep integration of medical practical 
problems with technological development. Overall, this graph 
reveals that the field of robotic surgery research is gradually 
shifting from equipment-centered engineering technology to 
comprehensive research oriented towards clinical 
application, data intelligence, and human-machine 
collaboration. 

 

 

 

 

 

 

 

 

 

Figure10. Keyword co-occurrence visualization map 

Furthermore, as shown in Figure 11, from the 
visualization results of the clustering network, it can be 
further observed that the current research on robotic surgery 
has formed several thematic clusters. Among them, clusters 
#0 "computer aided detection", #1 "minimally invasive 
surgery", #4 "design", and #6 "robot-assisted surgery" are 
relatively large in scale and closely related, representing the 
main directions of the integration of technology development 
and clinical practice in this field. At the same time, cluster #2 
"transoral robotic surgery" and cluster #18 "surgical skill 

Rank Count Centrality Year keyword 

1 179 0 2016 robotic surgery 

2 170 0 2019 artificial intelligence 

3 78 0.17 2015 machine learning 

4 69 0 2018 deep learning 

5 52 0 2018 surgery 

6 44 0.02 2017 
minimally invasive 
surgery 

7 43 0.05 2016 system 

8 42 0.02 2016 
robot-assisted 
surgery 

9 32 0.02 2018 outcm 

10 31 0.1 2015 performance 

11 31 0.11 2017 validation 

12 24 0.14 2015 augmented reality 

13 24 0.02 2017 laparoscopic surgery 

14 20 0.05 2019 cancer 

15 20 0.02 2017 tracking 



Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

157 

 

assessment" focus on specific surgical procedures and the 
evaluation of the surgeon's ability, demonstrating that the 
research is gradually expanding into more specialized areas 
such as disease-specific applications and quantitative 
assessment of surgical quality. 

 

 

 

 

 

 

 

 

 

 

Figure 11. Cluster map of keywords 

 

The sudden emergence of keywords serves as an 
important indicator for identifying emerging research trends 
and their evolution over time [9]. This study utilized 
CiteSpace to analyze the clustering of keywords, and Figure 
12 presents the results of keyword burst detection. The 
intensity and duration of each burst keyword represent a 
significant increase in research attention for this topic during 
a specific period [10]. As shown in Figure 12, from the 
perspective of burst intensity, "laparoscopic surgery" (with a 
burst intensity of 5.52) ranks first, with the burst period 
concentrated in 2021-2022, indicating that traditional 
minimally invasive surgery remains a key focus of the 
academic community in the context of intelligence; secondly, 
keywords such as "resection" and "recognition" related to 
specific surgical tasks and intraoperative perception also 
showed a significant increase after 2021. At the same time, 
keywords like "robot-assisted surgery", "computer vision", 
"accuracy", and "task analysis" emerged in 2022-2023 and 
continued until 2024, reflecting that research in this field is 
gradually shifting towards intraoperative intelligent 
perception and automated execution performance.  

 

 

 

 

 

 

 

 

 

 

 

 

Figure 12. The top 15 burst-detection keywords based on WoS data 

Additionally, the emergence of terms such as "deep 
learning methods", "model", and "medical robots and 
systems" indicates that the embedding of AI technology in 
surgical systems and algorithm optimization has become a 
key research hotspot. Overall, the burst keywords have 
shifted from the early topics of "system application" and 
"minimally invasive surgery" to "intraoperative intelligent 
perception", "algorithm efficiency", and "precise operation", 
marking that robotic surgery technology is entering a new 
stage centered on data-driven intelligent perception and 
decision control. 

4. Conclusion  

In summary, this study analyzed the evolution of robotic 
surgery technology through a systematic analysis using the 
CiteSpace tool based on 520 articles from the Web of Science 
database between 2014 and 2024. It revealed the evolution 
trajectory of this field from early clinical exploration to 
intelligent development. The study found that this field has 
formed a new form of multi-disciplinary collaboration among 
surgery, biomedical engineering, and computer science. 
Through keyword analysis, it was discovered that intelligent 
sensing, precise execution, and disease-specific applications 
are becoming new research directions, indicating that robotic 
surgery technology is evolving from an auxiliary tool to an 
intelligent system. These conclusions are supported by 
convergent evidence from multiple bibliometric indicators: 
publication growth patterns (520% increase 2014-2024), 
keyword evolution (shift from mechanical to AI terms), 
citation bursts (AI-related papers dominating 2022-2024), 
and network centrality patterns (emergence of 
computational hubs). Firstly, the research on robotic surgery 
technology shows a significant phased growth characteristic. 
From the budding period of 2014-2017 to the explosive 
period of 2022-2024, the surge in the number of articles 
indicates that interdisciplinary technologies such as artificial 
intelligence and machine learning are driving this field into a 
high-speed development stage. The research topics have 
gradually shifted from early clinical exploration to 
intelligence, networking, and precision. Secondly, 
interdisciplinary integration has become the core paradigm of 
the development of robotic surgery technology. From the 
dominance of surgery, biomedical engineering, and robotics 
to the increasingly significant contributions of computer 
science and electronic engineering. Interdisciplinary cross-
communication has not only been reflected in the technical 
development level but also extended to ethical norms and 
clinical evaluation dimensions, forming a new ecosystem of 
"technology - application - system" collaboration. Meanwhile, 
the national/regional and institutional collaboration network 
highlights the imbalance in the research landscape and the 
pivotal role of key hubs. Among them, the United States 
dominates with 36.73% of the publications, while Italy and 
France, with high centrality, become key hubs for academic 
cooperation. In terms of institutions, the University of London 
and Johns Hopkins University lead the global collaboration 
network with high output, focusing on AI legal frameworks, 
human-computer interaction, and remote operation 
technologies. Finally, keyword and emergent analysis further 
reveal emerging research directions. The frequent words 
"artificial intelligence" and "machine learning" indicate that 
the deep embedding of AI technology is the core of current 
research, while the emergent keywords "computer vision" 
and "deep learning methods" suggest that the future will 
focus on intelligent perception, algorithm optimization, and 
precise execution. At the same time, the incorporation of 



Y. Liu & MWM. Shafiei /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 148-158 

158 

 

disease-related keywords such as "human papillomavirus" 
marks that the research on robotic surgery is shifting from 
general technology development to customized applications 
in specific clinical scenarios. Overall, robotic surgery 
technology is undergoing a transformation from an "auxiliary 
tool" to an "intelligent system". Based on the bibliometric 
analysis results of this study, future researchers can further 
precisely identify emerging research frontiers, provide data 
reference for interdisciplinary cooperation, and promote the 
further development of research in this field. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
Datasets analyzed during the current study are available and 
can be provided upon a reasonable request from the 
corresponding author. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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