Diversity and inclusion: A hidden additional benefit of Open Data.

The recent imperative by the National Institutes of Health to share scientific data publicly underscores a significant shift in academic research. Effective as of January 2023, it emphasizes that transparency in data collection and dedicated efforts towards data sharing are prerequisites for translational research, from the lab to the bedside. Given the role of data access in mitigating potential bias in clinical models, we hypothesize that researchers who leverage open-access datasets rather than privately-owned ones are more diverse. In this brief report, we proposed to test this hypothesis in the transdisciplinary and expanding field of artificial intelligence (AI) for critical care. Specifically, we compared the diversity among authors of publications leveraging open datasets, such as the commonly used MIMIC and eICU databases, with that among authors of publications relying exclusively on private datasets, unavailable to other research investigators (e.g., electronic health records from ICU patients accessible only to Mayo Clinic analysts). To measure the extent of author diversity, we characterized gender balance as well as the presence of researchers from low- and middle-income countries (LMIC) and minority-serving institutions (MSI) located in the United States (US). Our comparative analysis revealed a greater contribution of authors from LMICs and MSIs among researchers leveraging open critical care datasets (treatment group) than among those relying exclusively on private data resources (control group). The participation of women was similar between the two groups, albeit slightly larger in the former. Notably, although over 70% of all articles included at least one author inferred to be a woman, less than 25% had a woman as a first or last author. Importantly, we found that the proportion of authors from LMICs was substantially higher in the treatment than in the control group (10.1% vs. 6.2%, p<0.001), including as first and last authors. Moreover, we found that the proportion of US-based authors affiliated with a MSI was 1.5 times higher among articles in the treatment than in the control group, suggesting that open data resources attract a larger pool of participants from minority groups (8.6% vs. 5.6%, p<0.001). Thus, our study highlights the valuable contribution of the Open Data strategy to underrepresented groups, while also quantifying persisting gender gaps in academic and clinical research at the intersection of computer science and healthcare. In doing so, we hope our work points to the importance of extending open data practices in deliberate and systematic ways.

Does diversity beget diversity? A scientometric analysis of over 150,000 studies and 49,000 authors published in high-impact medical journals between 2007 and 2022.

Background: Health research that significantly impacts global clinical practice and policy is often published in high-impact factor (IF) medical journals. These outlets play a pivotal role in the worldwide dissemination of novel medical knowledge. However, researchers identifying as women and those affiliated with institutions in low- and middle-income countries (LMIC) have been largely underrepresented in high-IF journals across multiple fields of medicine. To evaluate disparities in gender and geographical representation among authors who have published in any of five top general medical journals, we conducted scientometric analyses using a large-scale dataset extracted from the New England Journal of Medicine (NEJM), Journal of the American Medical Association (JAMA), The British Medical Journal (BMJ), The Lancet, and Nature Medicine. Methods: Author metadata from all articles published in the selected journals between 2007 and 2022 were collected using the DimensionsAI platform. The Genderize.io API was then utilized to infer each author's likely gender based on their extracted first name. The World Bank country classification was used to map countries associated with researcher affiliations to the LMIC or the high-income country (HIC) category. We characterized the overall gender and country income category representation across the medical journals. In addition, we computed article-level diversity metrics and contrasted their distributions across the journals. Findings: We studied 151,536 authors across 49,764 articles published in five top medical journals, over a long period spanning 15 years. On average, approximately one-third (33.1%) of the authors of a given paper were inferred to be women; this result was consistent across the journals we studied. Further, 86.6% of the teams were exclusively composed of HIC authors; in contrast, only 3.9% were exclusively composed of LMIC authors. The probability of serving as the first or last author was significantly higher if the author was inferred to be a man (18.1% vs 16.8%, P < .01) or was affiliated with an institution in a HIC (16.9% vs 15.5%, P < .01). Our primary finding reveals that having a diverse team promotes further diversity, within the same dimension (i.e., gender or geography) and across dimensions. Notably, papers with at least one woman among the authors were more likely to also involve at least two LMIC authors (11.7% versus 10.4% in baseline, P < .001; based on inferred gender); conversely, papers with at least one LMIC author were more likely to also involve at least two women (49.4% versus 37.6%, P < .001; based on inferred gender). Conclusion: We provide a scientometric framework to assess authorship diversity. Our research suggests that the inclusiveness of high-impact medical journals is limited in terms of both gender and geography. We advocate for medical journals to adopt policies and practices that promote greater diversity and collaborative research. In addition, our findings offer a first step towards understanding the composition of teams conducting medical research globally and an opportunity for individual authors to reflect on their own collaborative research practices and possibilities to cultivate more diverse partnerships in their work.

Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow.

Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.

Patient diversity and author representation in clinical studies supporting the Surviving Sepsis Campaign guidelines for management of sepsis and septic shock 2021: a systematic review of citations.

BACKGROUND: The generalizability of the Surviving Sepsis Campaign (SSC) guidelines to various patient populations and hospital settings has been debated. A quantitative assessment of the diversity and representation in the clinical evidence supporting the guidelines would help evaluate the generalizability of the recommendations and identify strategic research goals and priorities. In this study, we evaluated the diversity of patients in the original studies, in terms of sex, race/ethnicity, and geographical location. We also assessed diversity in sex and geographical representation among study first and last authors. METHODS: All clinical studies cited in support of the 2021 SSC adult guideline recommendations were identified. Original clinical studies were included, while editorials, reviews, non-clinical studies, and meta-analyses were excluded. For eligible studies, we recorded the proportion of male patients, percentage of each represented racial/ethnic subgroup (when available), and countries in which they were conducted. We also recorded the sex and location of the first and last authors. The World Bank classification was used to categorize countries. RESULTS: The SSC guidelines included six sections, with 85 recommendations based on 351 clinical studies. The proportion of male patients ranged from 47 to 62%. Most studies did not report the racial/ ethnic distribution of the included patients; when they did so, most were White patients (68-77%). Most studies were conducted in high-income countries (77-99%), which included Europe/Central Asia (33-66%) and North America (36-55%). Moreover, most first/last authors were males (55-93%) and from high-income countries (77-99%). CONCLUSIONS: To enhance the generalizability of the SCC guidelines, stakeholders should define strategies to enhance the diversity and representation in clinical studies. Though there was reasonable representation in sex among patients included in clinical studies, the evidence did not reflect diversity in the race/ethnicity and geographical locations. There was also lack of diversity among the first and last authors contributing to the evidence.

Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care.

Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capture, the increased capacity for data aggregation and analytic power, along with decreasing costs of genome sequencing and related biologic "omics", set the foundation and need for novel tools that can meaningfully process these data from multiple sources and of varying types. These advances provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multi-dimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are increasingly relevant concerns; critically, it is possible for AI to mirror the unconscious biases of the humans who generated these algorithms. Therefore, to avoid worsening existing health disparities, it is critical to employ a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this review, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.

Artificial intelligence for prediction of treatment outcomes in breast cancer: Systematic review of design, reporting standards, and bias.

BACKGROUND: Artificial intelligence (AI) has the potential to personalize treatment strategies for patients with cancer. However, current methodological weaknesses could limit clinical impact. We identified common limitations and suggested potential solutions to facilitate translation of AI to breast cancer management. METHODS: A systematic review was conducted in MEDLINE, Embase, SCOPUS, Google Scholar and PubMed Central in July 2021. Studies investigating the performance of AI to predict outcomes among patients undergoing treatment for breast cancer were included. Algorithm design and adherence to reporting standards were assessed following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. Risk of bias was assessed by using the Prediction model Risk Of Bias Assessment Tool (PROBAST), and correspondence with authors to assess data and code availability. RESULTS: Our search identified 1,124 studies, of which 64 were included: 58 had a retrospective study design, with 6 studies with a prospective design. Access to datasets and code was severely limited (unavailable in 77% and 88% of studies, respectively). On request, data and code were made available in 28% and 18% of cases, respectively. Ethnicity was often under-reported (not reported in 52 of 64, 81%), as was model calibration (63/64, 99%). The risk of bias was high in 72% (46/64) of the studies, especially because of analysis bias. CONCLUSION: Development of AI algorithms should involve external and prospective validation, with improved code and data availability to enhance reliability and translation of this promising approach. Protocol registration number: PROSPERO - CRD42022292495.

A polysomnography study examining the association between sleep and postoperative delirium in older hospitalized cardiac surgical patients.

Hospitalized older patients who undergo elective cardiac surgery with cardiopulmonary bypass are prone to postoperative delirium. Self-reported shorter sleep and longer sleep have been associated with impaired cognition. Few data exist to guide us on whether shorter or longer sleep is associated with postoperative delirium in this hospitalized cohort. This was a prospective, single-site, observational study of hospitalized patients (>60 years) scheduled to undergo elective major cardiac surgery with cardiopulmonary bypass (n = 16). We collected and analysed overnight polysomnography data using the Somté PSG device and assessed for delirium twice a day until postoperative day 3 using the long version of the confusion assessment method and a structured chart review. We also assessed subjective sleep quality using the Pittsburg Sleep Quality Index. The delirium median preoperative hospital stay of 9 [Q1, Q3: 7, 11] days was similar to the non-delirium preoperative hospital stay of 7 [4, 9] days (p = .154). The incidence of delirium was 45.5% (10/22) in the entire study cohort and 50% (8/16) in the final cohort with clean polysomnography data. The preoperative delirium median total sleep time of 323.8 [Q1, Q3: 280.3, 382.1] min was longer than the non-delirium median total sleep time of 254.3 [210.9, 278.1] min (p = .046). This was accounted for by a longer delirium median non-rapid eye movement (REM) stage 2 sleep duration of 282.3 [229.8, 328.8] min compared to the non-delirium median non-REM stage 2 sleep duration of 202.5 [174.4, 208.9] min (p = .012). Markov chain modelling confirmed these findings. There were no differences in measures of sleep quality assessed by the Pittsburg Sleep Quality Index. Polysomnography measures of sleep obtained the night preceding surgery in hospitalized older patients scheduled for elective major cardiac surgery with cardiopulmonary bypass are suggestive of an association between longer sleep duration and postoperative delirium.