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Abstract

Background

The growing role of artificial intelligence (AI) in healthcare, particularly radiology, requires its unbiased and fair development and implementation, starting with the constitution of the scientific community.

Purpose

To examine the gender and country distribution among academic editors in leading computer science and AI journals.

Material and Methods

This cross-sectional study analyzed the gender and country distribution among editors-in-chief, senior, and associate editors in all 75 Q1 computer science and AI journals in the Clarivate Journal Citations Report and SCImago Journal Ranking 2022. Gender was determined using an open-source algorithm (Gender Guesser™), selecting the gender with the highest calibrated probability.

Result

Among 4,948 editorial board members, women were underrepresented in all positions (editors-in-chief/senior editors/associate editors: 14%/18%/17%). The proportion of women correlated positively with the SCImago Journal Rank indicator (ρ = 0.329; p = .004). The U.S., the U.K., and China comprised 50% of editors, while Australia, Finland, Estonia, Denmark, the Netherlands, the U.K., Switzerland, and Slovenia had the highest women editor representation per million women population.

Conclusion

Our results highlight gender and geographic disparities on leading computer science and AI journal editorial boards, with women being underrepresented in all positions and a disproportional relationship between the Global North and South.

Keywords

artificial intelligence radiology gender equity bioethics computer science editorial policies

Introduction

The development, use, and popularity of artificial intelligence (AI) applications in radiology have increased rapidly in recent years.¹ As of 2022, the U.S. Food and Drug Administration had approved a total of over 500 AI- and machine learning-enabled medical devices, of which more than 75% (N = 391) are dedicated to the radiological discipline.² Among various use cases, these AI-powered tools can assist in medical image analysis reaching expert-level results, the transformation of free-text into structured radiology reports, image reconstruction, or radiation dose optimization.^3–6 Since a significant share of medical AI devices is related to radiology, scientific AI, and informatics journals publishing medical AI content can act as important intermediaries between AI developers, scientists, and radiologists, presenting, critically discussing, and disseminating the latest applications across the professional community.^2,7 However, while there are debates about bias, fairness, and diversity of radiological AI applications, there is currently a lack of research on the diversity of experts and scientists who develop new AI products and strategies, which extends to the academic publishing of AI literature.^8–10

Despite the fundamental role of (external) peer review in academic publishing to achieve an unbiased and fair expert evaluation of the studies conducted, editors of scientific journals hold one of the most powerful positions in academic research, as they evaluate the suitability of manuscripts in the first instance and have the final decision-making authority in the publication process.^11–13 Moreover, editorial board members guide and advise on publishing activities, directions, and processes and promote journals and research among their peers.¹⁴ Hence, the diversity of editorial board members is instrumental in making unbiased judgments, informing policies and best practices, and ultimately empowering diverse research, perspectives, and stakeholders.^15,16 In the context of medical AI journals, diverse editorial boards may foster the development and publication of more accurate and generalizable AI models that meet the needs of diverse patient populations, consequently enhancing diagnostic and treatment capabilities.^8,9,17

Although previous studies have examined gender and geographical representations of editorial board members or authorships in radiology and other disciplines affected by medical AI, research on the diversity of academic AI journals remains limited.^10,18,19 To promote diversity, address biases, and foster a more inclusive research community in the field of (medical) AI development and research, this study primarily aims to provide a first-time, up-to-date assessment of gender disparities and secondarily examines the geographical distribution among academic editors on the editorial boards of leading AI and computer science journals.

Material and methods

This cross-sectional study was conducted in accordance with the STROBE guidelines. Because this study was based on publicly available data, institutional review board approval was not required following our institution’s policies.

Data collection

We examined the gender and country distribution among editors-in-chief, senior, and associate editors of all Q1 journals in the category “Computer Science, Artificial Intelligence” of the Clarivate Journal Citations Report 2022 (N = 36) and each additional Q1 “Computer Science” journal in the top 50 ranks of the SCImago Journal Ranking 2022 (N = 39).^20,21

Gender and affiliation country of the academic editors were collected by three readers (FB, CR, LCA; N = 25 journals each). Gender was inferred by analyzing each editor’s full name and affiliation country with the open-source algorithm Gender Guesser™, selecting the gender with the highest calibrated probability.²²

Statistical analysis

Statistical analysis was performed using SPSS (version 28.0.1.0) and R (version 4.2.1) with the “tidyverse” and “sf” packages.²³ The “naturalearth” package and Wikipedia’s list of countries by sex ratio were used for mapping affiliation countries.²⁴ Median and range were reported for the proportion of women academic editors, impact factor (IF), journal citation indicator (JCI), total citations, and SCImago journal rank (SJR) indicator. Normal distribution was tested using the Kolmogorov–Smirnov test. Spearman's ρ was used to determine the correlation between the share of women academic editors and the aforementioned research impact measures. The Mann–Whitney U test was used to compare the impact measures and the proportion of women senior and associate editors between journals with only men and at least one woman editor-in-chief. Journals with missing data were excluded from sub-analyses. A two-sided p value <.05 was considered significant.

Results

Journal impact measures and origin

The SJR indicator was available for 73 of 75 journals (median: 3.84, range: 1.37–13.21). IF was available for 72 journals (median: 9.93, range: 3.92–29.23). Total citations number (median: 14,132, range: 434–174,343) and JCI (median: 2.15, range: 0.73–6.36) were available for 74 journals each. Most journals were registered in the U.S. (N = 43), followed by the U.K. (N = 15), Netherlands (N = 13), Switzerland (N = 2), Singapore (N = 1), and Germany (N = 1).

Gender diversity of editorial boards and association with impact measures

Table 1 displays the distribution of editorial board positions by gender and country. We collected data on gender for 4,948 academic editors, of whom 4,083 (83%) were classified as men and 865 (17%) as women. The median overall women share was 18% (range: 0%–60%). Overall, women editors were underrepresented in editor-in-chief (N =12/85 (14%); median: 0%, range: 0%–100%), senior (N = 85/460 (18%); median: 11%, range: 0%–100%), and associate editorial board positions (N = 768/4,403 (17%); median: 18%, range: 0%–67%). In total, 65 journals had one, and ten journals had two editor-in-chief positions. Seven of the twelve women editors-in-chief held the position alone, while five co-chaired the position with a man editor-in-chief.

Table 1.

Gender distribution of editors-in-chief, senior editors, associate editors, and overall by country.

Variable	Editors-in-chief		Senior editors		Associate editors		Overall
Variable	Men	Women	Men	Women	Men	Women	Men	Women
Total number (%)	73 (86)	12 (14)	375 (82)	85 (18)	3,635 (83)	768 (17)	4,084 (83)	865 (17)
Number by country (%)
United States	23 (85)	4 (15)	137 (78)	39 (22)	924 (78)	266 (22)	1,084 (78)	309 (22)
China	2 (67)	1 (33)	24 (89)	3 (11)	607 (85)	103 (15)	633 (86)	107 (14)
United Kingdom	7 (100)	0	32 (91)	3 (9)	245 (77)	72 (23)	284 (79)	75 (21)
Canada	2 (67)	1 (33)	16 (76)	5 (24)	142 (82)	31 (18)	160 (81)	37 (19)
Italy	3 (100)	0	15 (71)	6 (29)	172 (82)	39 (18)	190 (81)	45 (19)
Australia	7 (78)	2 (22)	14 (74)	5 (26)	140 (87.5)	20 (12.5)	161 (86)	27 (14)
France	3 (60)	2 (40)	17 (89)	2 (11)	128 (84)	25 (16)	148 (84)	29 (16)
Germany	5 (100)	0	9 (69)	4 (31)	112 (84)	21 (16)	126 (83)	25 (17)
Spain	3 (100)	0	7 (100)	0	108 (84)	21 (16)	118 (85)	21 (15)
Hong Kong	2 (100)	0	9 (82)	2 (18)	83 (86)	13 (14)	94 (86)	15 (14)
Taiwan	3 (75)	1 (25)	3 (75)	1 (25)	69 (95)	4 (5)	75 (93)	6 (7)
Singapore	3 (100)	0	8 (80)	2 (20)	77 (91)	8 (9)	88 (90)	10 (10)
Japan	2 (100)	0	7 (100)	0	94 (91)	9 (9)	103 (92)	9 (8)
Netherlands	0	0	6 (100)	0	61 (78)	17 (22)	67 (80)	17 (20)
India	1 (100)	0	7 (78)	2 (22)	84 (88)	11 (12)	92 (88)	13 (12)
Sweden	1 (100)	0	7 (100)	0	34 (83)	7 (17)	42 (86)	7 (14)
Switzerland	0	0	6 (100)	0	31 (77.5)	9 (22.5)	37 (80)	9 (20)
South Korea	1 (100)	0	3 (100)	0	44 (90)	5 (10)	48 (91)	5 (9)
Brazil	0	0	2 (67)	1 (33)	41 (72)	16 (28)	43 (72)	17 (28)
Belgium	0	0	5 (83)	1 (17)	27 (84)	5 (16)	32 (84)	6 (16)
Denmark	1 (100)	0	1 (25)	3 (75)	16 (80)	4 (20)	18 (72)	7 (28)
Norway	0	0	2 (100)	0	17 (81)	4 (19)	19 (83)	4 (17)
Israel	0	0	5 (100)	0	20 (83)	4 (17)	25 (86)	4 (14)
Finland	1 (100)	0	4 (80)	1 (20)	20 (77)	6 (23)	25 (78)	7 (22)
Austria	1 (100)	0	0	0	25 (96)	1 (4)	26 (96)	1 (4)
Greece	0	0	1 (100)	0	23 (92)	2 (8)	24 (92)	2 (8)
Portugal	0	0	3 (100)	0	25 (78)	7 (22)	28 (80)	7 (20)
Ireland	1 (50)	1 (50)	3 (100)	0	7 (78)	2 (22)	11 (79)	3 (21)
Poland	0	0	1 (100)	0	32 (97)	1 (3)	33 (97)	1 (3)
Mexico	0	0	0	0	28 (87.5)	4 (12.5)	28 (87.5)	4 (12.5)
Turkey	0	0	1 (50)	1 (50)	29 (85)	5 (15)	30 (83)	6 (17)
Chile	0	0	2 (100)	0	14 (87.5)	2 (12.5)	16 (89)	2 (11)
Qatar	0	0	2 (100)	0	15 (100)	0	17 (100)	0
Czech Republic	0	0	1 (100)	0	9 (69)	4 (31)	10 (71)	4 (29)
South Africa	0	0	1 (100)	0	11 (79)	3 (21)	12 (80)	3 (20)
Iran	0	0	2 (100)	0	8 (100)	0	10 (100)	0
Saudi Arabia	0	0	1 (100)	0	10 (91)	1 (9)	11 (92)	1 (8)
United Arab Emirates	0	0	5 (100)	0	2 (100)	0	7 (100)	0
Cyprus	0	0	0	0	6 (100)	0	6 (100)	0
Romania	0	0	0	0	9 (90)	1(10)	9 (90)	1 (10)
Russia	0	0	0	0	4 (100)	0	4 (100)	0
Egypt	0	0	0	0	6 (100)	0	6 (100)	0
Hungary	0	0	0	0	5 (100)	0	5 (100)	0
Lebanon	0	0	0	0	3 (100)	0	3 (100)	0
Malaysia	0	0	0	0	1 (17)	5 (83)	1(17)	5 (83)
Slovenia	0	0	1 (100)	0	17 (85)	3 (15)	18 (86)	3 (14)
Thailand	0	0	0	2 (100)	1 (50)	1 (50)	1(25)	3 (75)
Iceland	0	0	1 (100)	0	1 (100)	0	2 (100)	0
Bangladesh	0	0	0	0	2 (100)	0	2 (100)	0
Colombia	0	0	0	0	2 (100)	0	2 (100)	0
Estonia	0	0	0	0	2 (50)	2 (50)	2 (50)	2 (50)
Liechtenstein	0	0	0	0	2 (100)	0	2 (100)	0
Luxembourg	0	0	0	0	4 (100)	0	4 (100)	0
Pakistan	0	0	0	0	3 (100)	0	3 (100)	0
Argentina	0	0	1 (100)	0	1 (33)	2 (67)	2 (50)	2 (50)
Peru	0	0	0	0	1 (100)	0	1 (100)	0
Philippines	0	0	0	1 (100)	0	0	0	1 (100)
Sri Lanka	0	0	0	1 (100)	0	0	0	1 (100)
Tunisia	0	0	1 (100)	0	3 (100)	0	4 (100)	0
Ukraine	0	0	1 (100)	0	0	0	1 (100)	0
Guyana	0	0	0	0	1 (100)	0	1 (100)	0
Malta	0	0	0	0	1 (100)	0	1 (100)	0
Cuba	0	0	0	0	1 (100)	0	1 (100)	0
Azerbaijan	0	0	0	0	1 (100)	0	1 (100)	0
Jordan	0	0	0	0	1 (100)	0	1 (100)	0
Kuwait	0	0	0	0	1 (100)	0	1 (100)	0
Lithuania	0	0	0	0	1 (50)	1 (50)	1 (50)	1 (50)
Serbia	0	0	0	0	2 (100)	0	2 (100)	0
Macao	0	0	0	0	3 (100)	0	3 (100)	0
Slovakia	0	0	0	0	1 (100)	0	1 (100)	0
Uruguay	0	0	0	0	1 (100)	0	1 (100)	0
Vietnam	0	0	0	0	0	1 (100)	0	1 (100)
Not applicable	1 (100)	0	1 (100)	0	15 (100)	0	17 (100)	0

Fig. 1 illustrates all journals analyzed and their proportion of women editors compared to their respective IF, JCI, total citations, and SJR indicator. Out of 75 journals examined, 73 (97%) had editorial boards with a higher proportion of men, with one journal having no women on the editorial board. Of the remaining two journals, one featured an equal number of men and women academic editors, and one had a women-dominated editorial board. The women's share of editorial boards did not significantly correlate with IF (ρ = 0.123; p = .304), JCI (ρ = 0.218; p = .062), and total citation number (ρ = –0.104; p = .379), but positively correlated with the SJR indicator (ρ = 0.329; p = .004). IF, JCI, SJR indicator, total citation number, and the percentage of women editors in senior/associate editorial board positions did not significantly differ between journals with only men editors-in-chief and journals with at least one woman editor-in-chief (see Fig. 2).

Fig 1.

Percentage of women editorial board members among all computer science journals analyzed in relation to the corresponding impact factor (a), total citations (b), SCImago journal rank indicator (c), and journal citation indicator (d). Notes: (a) Radiology: Artificial Intelligence, Foundations, and Trends in Machine Learning, and Foundations and Trends in Computer Graphics and Vision did not receive an impact factor yet. b, (d) Radiology: Artificial Intelligence is not listed in the Journal Citation Report 2022. (c) Complex & Intelligent Systems and Advanced Intelligent Systems are not listed in the SCImago journal ranking.

Fig 2.

Boxplots show the distribution of values between computer science journals with only male editors-in-chief and at least one woman editor-in-chief for impact factor (a), total citations (b), journal citation indicator (c), SCImago journal rank indicator (d), percentage of women in senior editorial board positions (e), and percentage of women associate editors (f). Notes: p values display the results of the Mann–Whitney U test, which was used because of the nonparametric distribution of the data. There were no significant differences for all variables.

Country composition of editorial boards

Please refer to Table 1 to view the complete list of editors and editorial board positions per country. Overall, 72 different countries were identified. Most editors, regardless of their editorial board position, were affiliated with U.S. institutions (editors-in-chief: N = 27/84 (32%), senior editors: N= 176/459 (38%), and associate editors: N = 1,190/4,388 (27%)), followed by Australia for editors-in-chief (N = 9 (11%)), the U.K. for senior editors (N = 35 (8%)), and China for associate editorial board positions (N = 710 (16%)).

Fig. 3 depicts world maps representing the geographical distribution of academic editors in our study. When analyzing the geographical distribution of all editors or women editors in absolute terms, a pronounced representation from the U.S. and China was observed. However, this changes if the absolute number is normalized to editors per million inhabitants, where Canada, Australia, the U.K., and northern and southern European countries were predominantly represented alongside the U.S. but no longer China. When evaluating the number of women editors per million women population, Australia, Finland, Estonia, Denmark, the Netherlands, the U.K., Switzerland, and Slovenia accounted for the highest ratio of women.

Fig 3.

World maps display the absolute distribution of computer science journal editors per country (a, blue), number of editors per million inhabitants (b, green), number of women editors per country (c, grey/black), and number of women editors per million women (d, green/blue). Notes: While in absolute numbers, most editors (a) and women editors (c) are based in the U.S. and China, the number of editors per million inhabitants (b) shows that in relation to the population, Australia, Canada, the U.K., the northern and southern European countries alongside with the U.S. dominate, but no longer China. In contrast, the number of women editors per million women population (d) displays Australia, Finland, Estonia, Denmark, the Netherlands, the U.K., Switzerland, and Slovenia as the leading countries.

Discussion

In the present study, women editorial board members were underrepresented in 73 of 75 computer science and AI journals, with a particularly pronounced disparity in the position of editor-in-chief. The proportion of women on editorial boards showed a positive but weak correlation with the SJR indicator, while there was no significant correlation with IF, JCI, and total citations. Most editors were based in the U.S. and China, followed by Canada, Australia, the U.K., and northern and southern European countries, indicating a disproportionate relationship between the Global South and North and the most populous countries and residence of academic editors.

The underrepresentation of women on editorial boards extends beyond the fields of computer science and AI and is also evident in radiological research and other academic disciplines.^{18,19,25–27} Focusing on diversity research of radiology journal editorial boards, a 2018 study by Abdellatif et al. revealed that out of 460 editorial board members, only 88 (19%) were women, which is consistent with the overall women share of 17% (865 out of 4,948) in our study.¹⁸ In a recent 2022 study by Joshi et al., the representation of women on radiological editorial boards was even lower, with 2.4% (1 out of 42) for editors-in-chief, 17.8% (24 out of 135) for deputy editors, and 13.6% (345 out of 2,545) for all editorial board members.²⁵ On the other hand, Alkhawtani et al. showed that out of 57 radiological journals in 2021, only 5 (8.8%) had a woman as the sole editor-in-chief, while of 4,176 editorial board members, the median proportion of women was 21.5%, indicating a varying distribution of women depending on the investigated radiological journals and editorial positions.²⁶ Still, all studies display a significant lack of women in all editorial board positions of radiology journals. Given the exponential growth of medical AI-related content not only in computer science but also in academic radiology in recent years, this could likewise hinder the diversity of medical AI research, for example, by discouraging a variety of stakeholders from submitting their contributions, fostering an environment where dominant groups overshadow others, and limiting the range of research topics or inadvertently introducing biases in algorithms.^28–31 In medicine, this carries the risk of disparities in medical diagnostic, therapeutic, and prognostic outcomes for marginalized groups.⁸ However, in this respect, not only a diverse editorial board but also the diversity of peer review and double-blinding of manuscripts play a fundamental role.³² Moreover, especially in the field of medical AI, it is essential to emphasize the importance of diversity, equity, and equality in order to comply with the fundamental principles of biomedical ethics.³³

Finally, six journals stand out in our study for having a comparatively higher proportion of women editorial board members, represented by dark blue bars in Fig. 1, namely, Nature Biomedical Engineering (the only journal with more than 50% women editorial board members), npj Quantum Information, Molecular Systems Biology (all part of Nature portfolio), Internet and Higher Education, Journal Of Strategic Information Systems (both Elsevier), and Advanced Intelligent Systems (published by Wiley). While all three publishers have made public commitments to diversity in the past, they host 27 other journals with significantly lower percentages of women editorial board members in our study.^34–36 This discrepancy may suggest that the heightened presence of women in these journals is coincidental rather than a deliberate result of these commitments, underscoring the need for sustained efforts to consistently ensure diversity in the field of (medical) AI research.

In terms of geographical representations of academic editors, we could identify a disproportionate relationship between the most populous countries and where academic editors in computer science and AI journals are based. Despite accounting for 35% of the world population, China and India account for only 17% and 2% of editors, respectively, while 28% of editors are affiliated with the U.S., which accounts for only 4% of the world population.³⁷ It is noteworthy that India and the countries ranked fourth through tenth (Indonesia, Pakistan, Nigeria, Brazil, Bangladesh, Russia, and Mexico) in the 2022 world population ranking are not even among the ten countries with the most editors in our study. Moreover, this reveals an underrepresentation of editors from countries in the Global South. Possible explanations include the interplay between most computer science journals registered in the U.S. and Europe, popular university locations, and the migration of researchers from the Global South to the North.^38,39 In addition, researchers in the Global South are more likely to face challenges such as limited research funding, lack of infrastructure and training, and restricted access to academic networks compared to their peers from the Global North.^38–40 This inequity is even more concerning when contemplating the deployment of AI in healthcare within underrepresented regions. For example, countries in the Global South, which often face a dearth of medical professionals, could immensely benefit from AI-supported diagnostics, such as deep-learning algorithms for segmenting and classifying chest X-rays.^41,42 Yet, the usefulness of these tools is contingent on their accuracy and representativeness.⁴³ If the training data for medical AI tools is predominantly sourced from the Global North, there is a risk that algorithms are performing poorer in recognizing certain conditions inherent to other patient demographics.^42,44,45 This lack of inclusivity and potential bias in AI may hamper its applicability and success in varied global settings.

Limitations of our study include potential misclassification of gender using Gender Guesser™ due to varying associations between names and gender across cultural, regional, and historical contexts.⁴⁶ In addition, it is essential to note that Gender Guesser™ is designed to predict only binary gender, that is, categorizing individuals as either male or female. This inherently excludes individuals who do not strictly identify as such, including those who identify as non-binary, genderqueer, or other dimensions in the gender spectrum. Lastly, our cross-sectional analysis represents a snapshot and does not capture changes over time.

In conclusion, this paper highlights the underrepresentation of women academic editors in leading computer science and AI journals and emphasizes the unequal representation of editors from the Global South. Future research should further increase gender sensitivity, track progress, and help address gender and geographical imbalances to promote ethical, inclusive medical AI that reflects the diverse patient population it serves, aiming to reduce bias in medical diagnosis and treatment and empowering generalizable AI.

Footnotes

Acknowledgments

KKB is grateful for his participation in the Berlin Institute of Health (BIH) Charité Digital Clinician Scientist Program, funded by the Charité – Universitätsmedizin Berlin and the BIH.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical statement

ORCID iDs

Felix Busch

Sarah Keller

Avan Kader

Katharina Ziegeler

Keno K Bressem

Lisa C Adams

Data availability statement

The collected and analyzed dataset is openly available at: [non-permanent anonymized link until publication].

References

Rajpurkar

Chen

Banerjee

et al. AI in health and medicine. Nat Med 2022;28:31–38.

Food and Drug Administration . Artificial intelligence and machine learning (AI/ML)-Enabled medical devices. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices? (accessed 8 Aug 2023).

Tiu

Talius

Patel

et al. Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning. Nat Biomed Eng 2022;6:1399–1406.

Adams

Truhn

Busch

et al. Leveraging GPT-4 for post hoctransformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology 2023;307(4):230725.

Singh

Wang

et al. Artificial intelligence in image reconstruction: the change is here. Phys Med 2020;79:113–125.

CKC

. Artificial intelligence for radiation dose optimization in pediatric radiology: a systematic review. Children 2022;9:1044.

Maru Zhang

Re-ranking of high-impact AI journals based on H-Index. In: Zhang

Zhang

(eds) Engineering education and management. Springer, Berlin, Heidelberg: Lecture Notes in Electrical Engineering 2012;vol. 112:191–197.

Cirillo

Catuara

Guney

. Sex and gender bias in technology and artificial intelligence. 1st ed. Cambridge, MA: Academic Press, 2022, pp. 1–278.

Allen

Garg

. Diversity matters in academic radiology: acknowledging and addressing unconscious bias. J Am Coll Radiol 2016;13:1426–1432.

10.

Klinger

Mateos-Garcia

Stathoulopoulos

. A narrowing of AI research? ArXiv 2020;arXiv:2009.10385.

11.

Kelly

Sadeghieh

Adeli

. Peer review in scientific publications: benefits, critiques, & a survival guide. Electron. J Int Fed 2014;25:227–243.

12.

Palser

Lazerwitz

Fotopoulou

. Gender and geographical disparity in editorial boards of journals in psychology and neuroscience. Nat Neurosci 2021;25:272–279.

13.

Primack

Regan

Devictor

et al.

Are scientific editors reliable gatekeepers of the publication process?

Biol Conserv 2019;238: 108232.

14.

Elsevier . Editorial Boards. https://www.elsevier.com/editors/editorial-boards (accessed 6 Oct 2022).

15.

Kummar

Tancock

. Increasing (gender) diversity on a journal: a case study. https://www.elsevier.com/connect/editors-update/increasing-gender-diversity-on-a-journal-a-case-study (accessed 5 Aug 2023).

16.

Memon

Ahmed

Ghaffar

et al. Where are female editors from low-income and middle-income countries? A comprehensive assessment of gender, geographical distribution and country’s income group of editorial boards of top-ranked rehabilitation and sports science journals. Br J Sports Med 2022;56:458–468.

17.

Röösli

Bozkurt

Hernandez-Boussard

. Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model. Sci Data 2022;9:24.

18.

Abdellatif

Shao

Jalal

et al.

Novel Geographic Thematic Study of the largest radiology societies globally: how is gender structure biased within editorial boards?

AJR Am J Roentgenol 2019;213:2–7.

19.

Holman

Stuart-Fox

Hauser

. The gender gap in science: how long until women are equally represented? PLoS Biol 2018;16:e2004956.

20.

Clarivate™ . Journal citation reports. https://clarivate.com/webofsciencegroup/solutions/journal-citation-reports/ (accessed 3 Jan 2023).

21.

SCImago . SCImago journal & country rank. https://www.scimagojr.com/ (accessed 3 Jan 2023).

22.

Namsor™ Applied Onomastics . Gender Guesser™. https://gender-guesser.com (accessed 4 Feb 2023).

23.

R Core Team. R . A language and environment for statistical computing. https://www.R-project.org/ (accessed 9 Oct 2022).

24.

Wikipedia . List of countries by sex ratio https://en.wikipedia.org/wiki/List_of_countries_by_sex_ratio#Countries (accessed 20 Sept 2022).

25.

Joshi

Kong

et al. Female representation on radiology journal editorial boards around the world: geographical differences and temporal trends. Acad Radiol 2022;29:755–762.

26.

Alkhawtani

RHM

Kwee

. Gender diversity among editorial boards of radiology-related journals. Clin Imag 2021;75:30–33.

27.

Topaz

Sen

. Gender representation on journal editorial boards in the mathematical sciences. PLoS One 2016;11:e0161357.

28.

Perera

Perchik

et al.

Trends in medical artificial intelligence publications from 2000-2020: where does radiology stand?

Open J Clin Med Images 2022; 2:1052.

29.

Chen

Szolovits

Ghassemi

. Can AI help reduce disparities in general medical and mental health care? AMA J Ethics 2019;21:e167–179.

30.

Zou

Schiebinger

. AI can be sexist and racist — it’s time to make it fair. Nature 2018;559:324–326.

31.

Pham

Gamble

Hearn

et al. The need for ethnoracial equity in artificial intelligence for diabetes management: review and recommendations. J Med Internet Res 2021;23:e22320.

32.

Teplitskiy

Acuna

Elamrani-Raoult

et al. The sociology of scientific validity: how professional networks shape judgement in peer review. Res Policy 2018;9:1825–1841.

33.

Busch

Adams

Bressem

Biomedical ethical aspects towardsthe implementation of artificial intelligence in medical education. Med Sci Educ 2023;33(4):1007–1012.

34.

Nature Portfolio. Diversity commitment . https://www.nature.com/nature-portfolio/about/diversity-commitment (accessed 05 Aug 2023).

35.

Elsevier . Greater inclusion and diversity in health, science & research. https://beta.elsevier.com/about/inclusion-diversity-and-equity?trial=true (accessed 05 Aug 2023).

36.

Ricci

. Diversity, equity, and inclusion: why wiley? Why research Publishing? https://www.wiley.com/en-us/network/publishing/research-publishing/trending-stories/diversity-equity-and-inclusion-why-wiley-why-research-publishing (accessed 09 Aug 2023).

37.

The World Bank Group . Population, total - world bank data. https://data.worldbank.org/indicator/SP.POP.TOTL (accessed 03 Aug 2023).

38.

Selman

Pérez-Padilla

Pardo

. Problems encountered in high-level research in developing countries. Chest 1998;114:610–613.

39.

Abu-Zidan

Rizk

DEE

. Research in developing countries: problems and solutions. Int Urogynecol J 2005;16:174–175.

40.

Cash-Gibson

Rojas-Gualdrón

Pericàs

et al. Inequalities in global health inequalities research: a 50-year bibliometric analysis (1966-2015). PLoS One 2018;13:e0191901.

41.

Williams

Hornung

Nadimpalli

et al. Deep learning and its application for healthcare delivery in low and middle income countries. Front Artif Intell 2021;4:553987.

42.

Okolo

. Optimizing human-centered AI for healthcare in the global south. Patterns 2022;3:100421.

43.

Gilvary

Madhukar

Elkhader

et al. The Missing pieces of artificial intelligence in medicine. Trends Pharmacol Sci 2019;40:555–564.

44.

Wall

Saxena

Brown

. Artificial intelligence in the Global South (AI4D): potential and risks. ArXiv 2021;arXiv:2108.10093.

45.

Hagerty

Rubinov

. Global AI ethics: a review of the social impacts and ethical implications of artificial intelligence. ArXiv 2019;arXiv:1907.07892.

46.

Santamaría

Mihaljević

. Comparison and benchmark of name-to-gender inference services. PeerJ Comput Sci 2018;4:e156.

Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals

Abstract

Background

Purpose

Material and Methods

Result

Conclusion

Keywords

Introduction

Material and methods

Data collection

Statistical analysis

Results

Journal impact measures and origin

Gender diversity of editorial boards and association with impact measures

Country composition of editorial boards

Discussion

Footnotes

Acknowledgments

Declaration of conflicting interests

Funding

Ethical statement

ORCID iDs

Data availability statement

References