Discussion
We conducted a bibliometric analysis of the intersection of ophthalmology and AI between January 2018 and August 2021. Many aspects of the dataset were analysed in order to gain both quantitative and qualitative insights. In particular, investigation into countries of publication and their correlation (or lack thereof) with literature quality was performed, and it was found that smaller countries tended to produce more highly cited literature. There was a direct correlation between country population and gross quantity of published literature. Furthermore, countries with more international collaboration tended to have higher average article citations. With respect to research topics, the most common application of the AI technology to ophthalmology tended to be in diagnostic imaging.
Our findings suggested that the field of ophthalmology and AI has been growing at an exponential rate as predicted by Lotka’s law until 2020 when the scientific production dropped sharply.13 The authors hypothesise that there are two main reasons for this finding. First, it is likely that SARS-CoV-2 affected scientific production in the field of ophthalmology and AI as the broad scientific community shifted to focus on developing a body of research on the novel virus. Second, articles were only collected up to August 2021, and had the articles been collected up to December it is predicted that the growth rate of the field would have increased rather than decreased, though likely not with the same increase in rate as in previous years.
It was noted in our analysis that China and the USA collectively account for over 40% of the literature in the dataset. This is not surprising in consideration of the population size and large number of research institutions in both countries. Within the dataset there is an over-representation in the advanced economies of Southeast Asia, where Japan, Korea and Singapore accounted for more research in this field than the UK and Germany combined.
Popular AI ranking indices have consistently placed the USA and China at the top of research, development and implementation of new AI technologies over the past 5 years, with Japan and Korea ranking in the top 10.14 15 According to the Stanford AI index, in 2021, East Asia accounted for 26.7% of all published academic articles pertaining to AI globally, while the USA accounted for 14.0%.14 15 Further, global AI publications have seen a steep growth curve recently, with total international journal publications having increased 2.5 times since 2015. This rapid growth is seen in conjunction with an exponential increase in AI patent filings globally, with a compound annual growth rate of 76.9% between 2015 and 2021.16 As more research is published, more innovation is spurred, while new technology promotes new research, in a positive and fast accelerating feedback loop. In 2021, China held the greatest number of AI patent filings, while the USA had the most granted patents as a percentage of the world total filed and granted patents.16
We have used the number of citations as a measurement of literature impact. Previous studies have suggested that the correlation between citation numbers and value of scientific knowledge and influence is not perfect, and citations might also be influenced by factors such as author prominence and randomness.17 Although, there are important factors that should be considered when using number of citations as an absolute measure of literature quality,17 the large size of our data set may give an accurate overall picture of global impact.18 Our findings showed no statistically significant correlation between the gross number of publications for a country and mean number of citations. This result indicates that while China and the USA may produce nearly half of the articles in this field, they do not also attract the most citations. Our findings suggested that research from countries such as Austria, had the most citations per publication and high proportional international collaboration than China. It is well-established for scientometric characteristics that collaboration between institutions, in particular internationally, tends to produce research that is cited more frequently than less-collaborative work.19 As such China and the USA, although produce most publications they tend to collaborate less with institutions in other countries. The reasons behind this effect are multi-faceted and beyond the scope of this paper. Besides the cultural and geographic factors that would limit their international connections, both China and the USA have many universities within their own borders with whom to collaborate. In contrast, the high impact of smaller countries such as Singapore and Austria are surrounded by many other countries to collaborate with and have some of the highest citations-per-publication alongside a high proportion of MCPs.
We noted that the most collaborative countries, as well as those with the highest average citation impact, tend to be smaller countries in Europe with the exception of Singapore. As an Asian city-state with a British colonial heritage, Singapore’s cultural-linguistic connections both to Europe and to South-East Asia enable it to have the second-highest citations-per-paper of all the countries in this survey, showing how collaborations are more important than size. We also found that while China is the most productive country, it lags behind the only other country of comparable output (the USA) which tends to have more international collaborations. This is corroborated by two popular AI index reports, which find that while China leads the USA in gross publications, the USA ‘leads on the most significant research into cutting-edge developments’.14–16
From the co-occurrence network created diabetic retinopathy is most connected with the terms ‘deep learning’, ‘machine learning’ and ‘artificial intelligence’. Further, other popular terms relate to types of diagnostic imaging, such as ‘optical coherence tomography’ and ‘image segmentation’. This implies that the focus of the field is on applications of AI to diagnosis, and creation of algorithms for automating diagnosis and triage of ophthalmic diseases. Many medical fields follow a progression of care model, where diagnosis is the first step, followed by prognostication, development and administration of treatment protocols, and surgical management if necessary. As such, new technology may begin to develop first in the areas of need, in the case of the field of ophthalmology this is diagnosis and triage. Additionally, there is more cost and resource associated with research in robotics than computer research.20
