Quantitative analysis of publication

We found 615 papers on artificial intelligence in radiotherapy over the previous 20 years, based on our search approach. There were 188 “reviews” and 366 “articles” total among them. We divided the whole time into three phases: the first phase (2003–2017), the second phase (2018–2020), and the third phase (2021–2023) based on the annual growth rate of publications. Figure 2 presented the trends in publications. There were just a few studies on artificial intelligence in radiotherapy undertaken during the first phase (2003–2017), which produced a maximum of two publications (Fig. 2). However, the number of published papers climbed steadily in the second phase (2018–2020), averaging about 32 per year, suggesting the beginning of this field’s research. During the third phase (2021–2023), there was a noticeable increase in publications, with an average of about 144 papers produced annually. In particular, there were 73 relevant papers published in 2018, a 4.3-fold increase over the previous year. As research on artificial intelligence in radiation has continued to rise over the past ten years, the number of studies has increased steadily, reaching 166 in 2023. In comparison to the previous two phases, the third phase (2021–2023) showed a notable increase in the total number of published papers, suggesting the growing importance and interest of artificial intelligence in the field of radiation. Furthermore, in subsequent research, we speculate that the growth of publications may be closely related to the addition of new subdatasets and the expansion of scientific networks [11].

Fig. 2

The quantity of research publications on artificial intelligence in radiotherapy each year

Country and institutional analysis

There were 1144 institutions and 64 countries represented in these articles. Table 1 listed the top 10 countries, with a preponderance of Americans (2 out of 10) and Europeans (6 out of 10), including USA, China, Italy, Netherlands, England, Germany, France, Canada, Australia, and Belgium. USA (n = 167, 30.6%) has the most papers among these countries, followed by China (n = 90, 16.5%), Italy (n = 65, 11.9%), and Netherlands (n = 65, 11.9%). China and USA together account for almost half (47.1%) of all published papers. As an additional example, we created a collaborative network based on the amount of publications and inter-country linkages by screening and visualizing 64 nations based on publications with a number of one or more (Fig. 3). Interestingly, this data indicated a great deal of international cooperation. China, for example, maintained tight relations with USA, Italy, and France, while USA maintained active relations with Australia and Canada.

Three-fifths of the top 10 colleges were situated in the United States and the top 10 colleges represent five different countries. Interestingly, Maastricht University (n = 27, 5.0%), Duke University (n = 14, 2.6%), Harvard University (n = 13, 2.4%), and University of Toronto (n = 13, 2.4%), were the top four universities in terms of published papers. To further explain, we built a collaborative network based on publication volume and institutional ties, and we picked 139 institutions for visualization, guaranteeing a minimum publication count of three (Fig. 4).

Table 1 Top 10 countries and institutions on research of artificial intelligence in radiotherapyFig. 3

The geographical dispersion and visual representation of nations about AI in radiotherapy

Fig. 4

An illustration of research institutes focused on AI in radiotherapy

Journals and co-cited journals analysis

213 journals contained publications about artificial intelligence in radiation therapy. Notably, Table 2 demonstrated that, with 45 publications published, Frontiers in Oncology is first (n = 45, 8.2%), followed by Radiotherapy and oncology (n = 32, 5.9%), Medical Physics (n = 26, 4.8%), and Cancers (n = 25, 4.6%). Medical Physics was the most cited journal among the top 10 (Citations = 2275), closely followed by International Journal of Radiation Oncology (Citations = 1921). After doing this analysis, we were able to display the journal network map (Fig. 5A) and identify 67 journals that had at least two relevant publications. The relationship between cancer, Radiotherapy and oncology, and medical physics was shown in the diagram as one of active citation. Furthermore, our analysis showed that, in 2020, Radiotherapy and oncology was the most often cited journal concerning artificial intelligence in radiotherapy, and it was projected that citations related to cancer will rise until 2023.

Table 2 Top 10 journals and co-cited journals for artificial intelligence in radiotherapy

Table 2 showed the top 10 journals overall in terms of citations, with four journals having more than 1300 citations each. With a total citation count of 2275, medical physics was the most referenced field. It was followed by the International Journal of Radiation Oncology (total citation = 1921), Radiotherapy and Oncology (total citation = 1617), and Physics in Medicine and Biology (total citation = 1394). A co-citation network map was created after we conducted additional analysis on 158 journals with more than 30 co-citations (Fig. 5B). The graphic showed that the International Journal of Radiation Oncology had a favorable co-citation connection with eminent publications like Frontiers in Oncology and Medical Physics. This implied that International Journal of Radiation Oncology had close ties to other publications within the area, which highlighted the importance of the journal in research.

With the cluster of citing journals on the left and the cluster of cited journals on the right, the double-graph superposition of journals illustrated the citation link between journals and co-cited journals (Fig. 6). The orange and green paths in this visualization stood for the various primary cited paths, respectively. This indicated that the majority of literature references in the fields of molecular biology/immunology and medicine/medical/clinical originated from these journals when it referred to molecular biology/genetics research or health/nursing/medicine. This highlighted the interdisciplinary nature of study in this topic and showed a strong connection and influence between various fields.

Fig. 5

The display of artificial intelligence in radiotherapy research publications (A) and co-cited journals (B)

Fig. 6

The journals’ dual-map overlay on artificial intelligence research in radiotherapy

Authors and co-cited authors analysis

In all, 3556 writers took part in the research on AI in radiation treatment. Three writers published more than nine publications between them among the top ten: Dekker Andre published ten, Valentini Vincenzo and Boldrini Luca each published nine (Table 3). After that, we chose authors who had one or more articles published, and we created a collaborative network around those authors (Fig. 7A).

Two writers out of the 18,401 co-cited authors received more than 80 citations (Table 3). Philippe Lambin (n = 85) was the most often referenced author, followed by Dan Nguyen (n = 83), Issam Elnaqa (n = 79), and Gilmer Valdes (n = 79). After identifying writers who had at least 16 co-citations, we produced a co-citation network diagram (Fig. 7B). The figure showed the active cooperation of Issam Elnaqa, Philippe Lambin, and Gilmer Valdes, among other co-cited writers. This suggested a high degree of collaboration and interaction between researchers in the field, which will further develop our understanding of artificial intelligence in radiotherapy and its application.

Fig. 7

The illustration of the research on artificial intelligence in radiotherapy by authors (A) and co-cited authors (B)

Table 3 Top 10 authors and co-cited authors on research of artificial intelligence in radiotherapyCo-cited references analysis

A total of 25,181 papers about artificial intelligence in radiotherapy have been cited within the last 20 years. All ten of the most frequently cited works (Table 4 and 5) had at least 28 citations, and two of them had as least 40 citations. To create a co-citation network diagram (Fig. 8), we chose literatures with a total citation quantity of at least 12 and included 158 co-cited references. This graphic facilitated further research and analysis of important themes and trends by offering a thorough perspective of the connections and influences between numerous research papers in the subject.

Fig. 8

The display of co-cited references of artificial intelligence research in radiotherapy

Table 4 Top 10 co-cited references on research of artificial intelligence in radiotherapyReference with citation bursts analysis

Works with explosive citation were those that academics in a particular discipline had quoted a lot over a given length of time. CiteSpace found four papers in our analysis with significant bursts of citations (Fig. 9). Strong citation epidemics were represented by the red bar in Fig. 9. The eruption of references in citations started in 2015 and continued until 2016. Suzani, Amin, et al.’s paper, “Fast Automatic Vertebrae Detection and Localization in Pathological CT Scans - A Deep Learning Approach”, was published in the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), and it contained the strongest literature cited for the outburst (intensity = 6.64). “Evaluation of a knowledge-based planning solution for head and neck cancer” was the title of the reference with the second strongest citation burst (strength = 3.52).

Fig. 9

The top 4 sources with powerful, eye-catching citations. High citations for the year are indicated by red bars

Table 5 The main research content of 4 highly cited literaturesHotspots and frontiers analysis

With the use of keyword co-occurrence analysis, we may quickly pinpoint areas of study interest within a given topic. The top 20 keywords pertaining to artificial intelligence in radiotherapy were displayed in Table 6. Of them, “Deep learning” and “Machine learning” came up more than a hundred times, suggesting that they were important areas for future study on artificial intelligence in radiotherapy. This report offered insightful information about the hot subjects and areas of interest in the nexus of radiation and artificial intelligence.

Using VOSviewer, we did cluster analysis and filtered terms that appeared three times or more (Fig. 10A). The intensity of the relationship between keywords was indicated by the thickness of the lines connecting nodes. We found four two clusters, each corresponding to different study directions, as shown in Fig. 10A. The keywords that were included in the pink cluster include radiotherapy and artificial intelligence. Data science, big data, and bioinformatics were the keywords found in the purple clusters. The most prolonged period of artificial intelligence research in radiotherapy had been focused on normal tissue, according to a trending topic analysis of keywords (Fig. 10B). By 2023, autocontouring algorithms would be the primary area of study.

Fig. 10

The visualization of cluster analysis of keywords (A) and trend topic analysis (B)

Table 6 Top 20 keywords on research of artificial intelligence in radiotherapy