Canada

Cardiovascular diseases remain a leading cause of morbidity, making timely and accurate diagnosis essential for improving patient outcomes. However, sex-based disparities in the diagnosis of cardiovascular disease lead to increased risk of misdiagnosis, and subsequent undertreatment, in females. As artificial intelligence (AI), machine learning (ML), and large language models (LLMs) become increasingly integrated into clinical decision-making. It is important to determine whether these tools reduce or reproduce existing biases in existing medical data. This manuscript reviews evidence on sex-based bias in AI-assisted cardiovascular diagnosis. A two-phase search strategy was used to identify relevant literature from OMNI McMaster, Google Scholarly, PubMed, and Web of Science. Initial keywords produced fifty articles, which were screened using an inclusion and exclusion criteria related to human participants, clinical cardiovascular populations, AI or ML use, and sex-stratified evaluation. Fifteen articles were selected for detailed analysis. Across the reviewed literature, findings suggest sex-based disparities exist in both traditional clinical and AI-supported diagnosis. These findings highlight the need for further sex-stratified model evaluation, more representative data, and bias-mitigation strategies before AI tools are more widely implemented. Overall, AI has the potential to improve diagnosis, but without careful training and bias control, it may exaggerate existing gender-based biases.

Equitable Health Technologies: Overcoming Biases and False Inferences in AI Diagnostics and Drug Discovery

Biases In LLMs for Clinical Diagnosis of General Cardiovascular Disease

sex-based disparities

cardiovascular disease

specificity

llms

sensitivity

The OWSD General Assembly and international conference will be held entirely online for the first time in OWSD's 28-year history, allowing as many of OWSD's 7600+ members to participate as possible.

The conference theme is 'Women, science and development', exploring three areas of relevance: 1) the importance of basic sciences for development; 2) the impact of applied sciences on development and 3) including sex and gender as variables in scientific research.


**Keynote speakers include:** 
* Fabiola Gianotti, Director-General of CERN; 
* Bina Agarwal, an internationally celebrated economist specializing in the political economy of gender, poverty and inequality; and 
* Angela Saini, award-winning author of Inferior: How Science Got Women Wrong and the New Research That’s Rewriting the Story, Superior: The Return of Race Science, and other books. 

OWSD members will present their own research in the three themes during poster sessions throughout the conference. In addition, members will also present their own scientific and personal responses to the COVID-19 pandemic. All members will also have the opportunity to take part in a workshop on increasing their visibility and impact as a scientist, with a session on using podcasts for storytelling by Joy Owango and Adelle Onyango of the Training Centre in Communication (TCC Africa), followed by a session on filmmaking for impact by Nicole Leghissa, OWSD video consultant.

The conference will include a handover of the previous OWSD Executive Board to the new members, elected in June-July 2021. On the last day of the conference (19 November), the OWSD Italy Country Network will be launched. **This will be an in-person event in Trieste.** Italian government representatives and donors are invited to attend. The Trieste event will be coordinated with similar in person events being held in OWSD countries throughout the developing world and streamed online. At least 2,000 OWSD members, women scientists from developing countries around the world, are expected to participate online in the General Assembly and conference.

 

**ABOUT THE PROGRAMME**

In order to approach the complex and wide-ranging challenges of the 2030 Agenda for Sustainable Development, societies around the world must access and harness all the talent and experience available to them. The concerns and perceptions of the greatest number of all possible actors and recipients must be considered in order that the most relevant issues and effective solutions are found. Women are considerably underrepresented as researchers in STEM subjects around the globe; according to UNESCO Institute of Statistics (UIS) data, in 2019 the global average percentage of women researchers was still below 30%. Yet women typically have first hand specialist knowledge of the day-to-day challenges diverse individuals face within communities, and can be key informers in the design and development of their solutions. Moreover, since women are most often the providers of informal community support, if they are not fully informed—and if solutions are developed without their consultation—it may be the case that products, services and solutions are not relevant or appropriate. Technological advances may mean improved machines on large farms, for example, potentially increasing food production, but since the key points of food insecurity in the world are on small-scale farms managed by women throughout the Global South, such capital investments will have little impact in the areas where food production is most critical—and where women’s knowledgeable implementation of appropriate innovative technologies could make the most difference.

 
 


**Keynote Speakers:**
* Fabiola Gianotti, Director-General of CERN; 
* Bina Agarwal, an internationally celebrated economist specializing in the political economy of gender, poverty and inequality; and 
* Angela Saini, award-winning author of Inferior: How Science Got Women Wrong and the New Research That’s Rewriting the Story, Superior: The Return of Race Science, and other books. 

**Eminent Guests:**
* Elena Bonetti, Italian Minister for Family and Equal Opportunities
* Maria Cristina Messa,	Italian Minister of Universities and Research
* Shamila Nair-Bedouelle,	Assistant Director General for Natural Sciences, UNESCO
* Maria Chiara Carrozza,	President, National Research Council of Italy
* Kerstin Jonsson Cisse,	Head of Unit for Globally Sustainable Economic Development, Sida - Sweden
* Naser Faruqui,	Director of Education and Science at IDRC - Canada
* Ylann Schemm,	Director, Elsevier Foundation
* Alberto Quadrio Curzio,	President Emeritus of the Italian Academy of Sciences and Humanities 
* Mohamed Hassan,	President, TWAS the World Academy of Sciences

**Chairs, panelists, interviewers and other speakers:**
Jennifer Thomson, Tonya Blowers, Rana Dajani, Shobhana Narasimhan, Joy Owango, Elizabeth Pollitzer, Kausila Timsina, Elizabeth Bandason, Susana Arrechea, Reinabelle Reyes, Dorine Odongo, Elizabeth Pollitzer, Adelle Onyango, Nicole Leghissa, Jana Rodriguez Hertz, Maryse Nkoua Ngavouka, Kate Shaw, Chao Mbogo, Prativa Pandey, Edna Patricia Matta-Camacho, Amira Shaheen, Wezi Nyambili, Ana Lucía Martinez Abarca, Mashura Shammi, Therese Umuhoza, Dionica Gamboa, Marian Nkansah, Babalola Olubukola Oluranti, Esi Awuah, Fortunate Farirai, Atya Kapley, Hasin Anupama Azhari, Huda Basaleem, Nashwa Eassa, Shymaa Enany, Kleinsy Bonilla, Patricia Castillo-Briceno, Fang Xin

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OWSD 6th GENERAL ASSEMBLY AND INTERNATIONAL CONFERENCE

The conference theme is 'Women, science and development', exploring three areas of relevance: the importance of basic sciences for development; the impact of applied sciences on development, and including sex and gender as variables in scientific research.

technical paper

As AI transforms healthcare, ensuring reliability is critical. However, systems learning from existing data often reflect gaps or inconsistencies, leading to false inferences, diagnostic errors, and unequal performance across patient groups.

Open Data is pivotal for transparency and accountability. Biases emerging from unrepresentative datasets or flawed assumptions can perpetuate health disparities.

Leveraging public data on genomics, clinical trials, and social determinants allows researchers to audit and mitigate these risks effectively.

We task undergraduate and graduate students to harness data science to:

* Detect, reduce, and prevent bias in diagnostics and drug discovery;

* Develop data-driven tools that increase fairness and inclusivity;

* Innovate bias-aware model evaluation techniques to build trustworthy AI systems.

As AI transforms healthcare, ensuring reliability is critical. However, systems learning from existing data often reflect gaps or inconsistencies, leading to false inferences, diagnostic errors, and unequal performance across patient groups.

Artificial Intelligence (AI) systems for skin-disease classification can perform unevenly across skin tones, risking wider disparities in dermatological care. Using Fitzpatrick17k, we trained a Swin Transformer classifier and observed a 26.4-percentage-point accuracy gap across Fitzpatrick skin-type groups. This gap was non-monotonic: accuracy was lowest for the lightest group (41.9%) and highest for a mid-tone group (68.3%), not following a simple lighter-versus-darker pattern. We then tested whether dermatology-specific transfer learning improves accuracy, fairness, and cross-dataset generalization. We standardized five public datasets (Fitzpatrick17k, DermaCon-IN, Derm1M, PASSION, DDI) to a unified eight-class taxonomy and evaluated staged transfer learning, large-scale pretraining on Derm1M, and concept-bottleneck models. Cross-dataset fine-tuning slightly improved accuracy but widened the skin-tone gap from 26.4% to 29.9%. Derm1M pretraining raised Top-5 accuracy and macro AUC by 1.6 and 7.0 percentage points but did not improve Top-1 accuracy or reduce the skin-tone gap, and external validation on PASSION and DDI showed marked performance declines, indicating limited robustness under dataset shift. Concept-bottleneck models matched baseline accuracy while adding interpretability. Persistent inflammatory-versus-infectious confusion and frequent high-confidence errors remained. These results show that taxonomy standardization and domain-specific pretraining, though necessary for combining fragmented datasets, are not sufficient to close fairness gaps; explicit fairness- and imbalance-aware methods with multi-dataset training are the needed next steps.

Skin-Tone Fairness in Dermatology AI: Limits of Pretraining and Harmonization

Protein–protein interactions (PPIs) are a cornerstone of disease mechanisms, drug synthesis, and therapeutic discovery. Protein-protein binding affinity is a quantitative descriptor of PPIs which poses an important computational challenge. Structure prediction systems such as AlphaFold2, AlphaFold3, and ColabFold generate ranked candidate structures, while structure-based predictors such as ProAffinity-GNN estimate the binding affinity directly from a complex structure. In the common screening setting, where measured affinities are not yet available, we ask how stable the downstream estimates are across ranked predictions and prediction methods. For each complex, we score the top five structures per method and compare estimates using rank-wise, method-wise, and agreement statistics. We assess the pipeline's internal consistency and sensitivity. Estimates are highly stable to rank-choice but markedly more sensitive to the upstream prediction method. This inter-method effect is driven almost entirely by AlphaFold2, which produces systematically lower estimates, while AlphaFold3 and ColabFold agree closely with no detectable difference. The AF2 inputs are assembled from independently folded chains rather than jointly folded multimers, which we argue is the most likely driver. Estimates from different prediction methods should not be mixed, while scoring one representative rank may reduce downstream inference.

Rank and Method Variance in ProAffinity-GNN Binding Affinity Estimates from AlphaFold Variants

Antimicrobial peptides (AMPs) are short polypeptides that naturally exist in multicellular organisms as part of the immune defence system against microbial infections. Due to their broad-spectrum inhibitory effects on microbes, including bacteria, they pose a challenge for microbes to develop resistance to, making them an alternative to antimicrobial molecules used today and thereby mitigating antimicrobial resistance. Recent studies have investigated various Artificial Intelligence (AI) models to predict and generate AMPs for use against microbial infections. Among these approaches, shallow and deep learning models are proposed. However, one of the biggest issues with using generative AI methods is the increase in false positives, often stemming from systematic bias in data sampling during machine learning model training, which eventually fails when tested experimentally. This highlights the necessity of bias-aware AMP identification. Consequently, scientists should use more accurate performance metrics to evaluate models. Several approaches have been proposed to reduce false-positive AMP predictions, including benchmarking and reproducibility standards that promote fair evaluation and comparison across models. Furthermore, advanced models such as DeepAMPNet allow the integration of sequence, structural, and evolutionary information, enabling a more comprehensive analysis of AMPs. To improve the reliability of future models, representative datasets, more robust external validation, and experimental feedback should be incorporated. Overall, AMPs are an integral part of the antibiotic discovery pipeline. AI-driven approaches have significant potential to revolutionize AMP discovery; however, their effectiveness depends on proper optimization and validation.

Bias-Aware AMP Discovery: Mitigating False Positives in Generative Antimicrobial Models

Artificial intelligence (AI) screening tools are increasingly integrated into clinical decision-making. However, these tools are trained on structured clinical data that omit subgroup level disparities, risking inequitable performance across heterogeneous patient subpopulations. Polycystic ovarian syndrome (PCOS), characterized by menstrual irregularity and overlapping metabolic, endocrine, and inflammatory pathways, affects approximately 8-13% of reproductive-aged women. This study examined whether incorporating metabolic, inflammatory, and endocrine markers improves screening accuracy and subgroup equity in AI-based PCOS screening tools. Using the National Health and Nutrition Examination Survey (NHANES) 2017-2018 cycle, ten datasets spanning reproductive health, demographic, and metabolic/hormonal variables were merged and limited to reproductive-aged women (18-45 years old). Rotterdam criteria were approximated using menstrual irregularity, elevated testosterone, and elevated AMH/LH as a substitute for the lacking ultrasound-confirmed polycystic ovary morphology in NHANES. Two binary logistic regression models were developed: Model 1 used hormonal and menstrual features only, while Model 2 added metabolic and inflammatory features. Model 2 performed substantially better than Model 1, with lower AIC (984.59 to 279.73) and residual deviance (966.58 to 233.73). Across both models, menstrual regularity, testosterone, and luteinizing hormone significantly predicted PCOS risk (p < 0.001). Gene expression data (GEO accession GSE114419) comparing ovarian granulosa cell transcriptomes between PCOS patients and controls (n = 3) were analyzed to find 231 transcripts with fold-change greater than 2, providing biological context for hormonal markers used in the Rotterdam criteria. The method was translated into a publicly accessible online risk calculator to assess PCOS risk.

Uncovering Metabolic/Inflammatory Bias in AI Screening Models for Polycystic Ovarian Syndrome

Diabetes affects an ever-growing number of patients, and disparities in how they are diagnosed, treated and managed contribute to unequal outcomes regarding their health. In this study, demographic and socio-economic factors are examined to assess their effects on glycemic control, diabetes diagnosis, and access to care among individuals of similar A1c levels. These analyses were achieved by using data from the National Health and Nutrition Examination Survey (NHANES). First, among participants diagnosed with diabetes, ordinary least squares regression revealed that insurance status and education were positively correlated with glycemic control. Second, among the full sample, a logistic regression indicated that Mexican Americans and Non-Hispanic Black individuals had a markedly higher likelihood of being undiagnosed diabetics. Finally, 93.5% of participants displaying poor glycemic control reported having a usual place of care; however, the few lacking access were not evenly distributed. This study highlights the importance of addressing social factors that influence diabetes prevention and management.

Clinical Needs Fail to Equalise Access in Diabetes Care

Cardiovascular disease (CVD) is the leading cause of death worldwide. CVD risk can be predicted by traditional risk factor scoring systems. However, these models are limited by unequal performance across demographic groups. While alternative AI-based risk models are increasingly used for CVD risk prediction, it remains to be seen if they amplify these disparities. This study trained a XGBoost machine learning classifier on the National Health and Nutrition Examination Survey (NHANES) 2017–2018, a nationally representative sample of 9,254 American adults, to classify prevalent CVD. We evaluated model performance across sex, race/ethnicity, income, and age subgroups and benchmarked results against the Framingham Risk Score, a tool used for cardiovascular risk prediction. The model achieved strong overall discrimination with a ROC-AUC of 0.911 and a PR-AUC of 0.405, outperforming the Framingham Risk Score on all primary metrics. However, subgroup analysis revealed meaningful performance disparities. Female participants had a false negative rate of 0.629 compared to 0.421 in male participants, meaning nearly two thirds of women with CVD were missed. Low-income participants showed the lowest sensitivity at 0.386. Among Non-Hispanic Black participants, XGBoost offered no improvement over the Framingham Risk Score. These findings demonstrate that strong average performance does not guarantee fair performance across demographic groups. AI models trained on data reflecting historical inequities in healthcare access and diagnosis may risk encoding and perpetuating those same inequities. Systematic subgroup evaluation should be a minimum standard for any CVD classification model considered for clinical deployment.

Performance disparities across demographic subgroups in XGBoost-based classification of prevalent cardiovascular disease

Cancer survival prediction models are increasingly able to predict cancer survival using large sets of clinical and molecular data that often rely on the patient’s chronological age. However, chronological age alone may neglect important variables between patient subgroups, which could be captured through epigenetic age, a DNA methylation-based estimate of biological aging. This study investigated whether epigenetic age improves cancer survival prediction and subgroup equity for 2,470 patients across four cancer types: breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), lung adenocarcinoma (LUAD), and liver hepatocellular carcinoma (LIHC). TCGA methylation and clinical datasets were used to compute epigenetic age with the Horvath Clock, a DNA-methylation-based age estimator. Epigenetic age acceleration variables were then generated and used to compare three random forest models: Model A, which used chronological age and standard variables; Model B, which used epigenetic age and epigenetic age acceleration (EAA) variables; and Model C, which combined chronological and epigenetic age features. Performance and subgroup fairness of the models were evaluated by AUC, sensitivity, specificity, net reclassification improvement, and statistical tests. Breast cancer and lung cancer datasets showed significant racial differences in biological aging patterns. In lung cancer, Black patients showed a 13.39-year larger tumor-normal biological aging gap than White patients. Black breast cancer patients benefited more from the combined model than White patients, with higher net reclassification improvement. The above results imply that using epigenetic age in survival prediction models for cancers can improve fairness-related performance without compromising predictive performance.

Two ages, One patient: epigenetic age and equity in cancer survival prediction

Accurate demographic representation in clinical trials is crucial for data generalizability, yet reporting for specific ethnicities remains under-examined. Using a Python-based algorithm, this study evaluated the visibility of South Asian participants across 178 global ribociclib HER2-negative breast cancer trial records extracted from ClinicalTrials.gov. A novel, four-level rule-based framework, the South Asian Visibility (SAV) score, was developed to quantify reporting granularity, ranging from 0 (no demographic reporting) to 3 (explicit subgroup reporting). Natural language processing (NLP) evaluated target keyword frequencies, while non-parametric tests assessed relationships between study scale, design, and visibility. The analysis revealed a severe reporting deficit: 161 studies (90.4%) provided zero public demographic data (SAV score 0), whereas only six (3.4%) explicitly isolated South Asian subgroups (SAV score 3). Furthermore, sample size demonstrated a negligible, non-significant correlation with visibility scores (r_s = 0. 072, p = 0. 3491) , confirming that larger research scales do not inherently improve data transparency. NLP analysis supported this observation, yielding zero text mentions of "Asian" or "South Asian" despite frequent references to "race" and "ethnicity." Ultimately, reliance on broad, combined racial categories severely masks South Asian representation in clinical data. This reporting gap may limit the assessment of generalizability of oncological findings and threatens the equity of downstream data-driven technologies, such as precision oncology models. Mandating subgroup-level demographic reporting standards across global clinical registries is vital to achieving equitable health outcomes.

South Asian Visibility in Ribociclib Clinical Trials for HER2-Negative Breast Cancer: An Algorithmic Assessment of Demographic Reporting

Background: Low-cost screening models may expand access to chronic disease screening, but acceptable overall performance can conceal false-negative classifications in population subgroups that may lead to false reassurance. Objective: We evaluated whether a low-cost model for the composite chronic disease outcome produced unequal false-negative rates across demographic and socioeconomic groups and acceptable overall calibration. Methods: We analyzed 5,569 adults aged 20 years or older in the 2017–2018 National Health and Nutrition Examination Survey. Predictors included demographic, socioeconomic, behavioral, self-rated health, and body mass index variables. The outcome indicated current evidence of diabetes, an elevated blood-pressure indicator, cardiovascular disease or stroke, or chronic kidney disease. Logistic regression was the primary model and random forest was the benchmark. Performance was evaluated in a stratified 30% test set. Results: The composite outcome was present in 3,601 participants (64.7%). Logistic regression achieved an AUC of 0.836 (95% CI, 0.816–0.856), sensitivity of 0.856 (0.833–0.875), specificity of 0.624 (0.585–0.662), and false-negative rate of 0.144 (0.125–0.167). Random forest achieved an AUC of 0.821 (0.800–0.842) and false-negative rate of 0.117 (0.099–0.137). Among outcome-positive adults aged 20–39 years, false-negative rates were 0.684 (0.615–0.747) for logistic regression and 0.535 (0.463–0.605) for random forest. Logistic regression had better overall calibration. Conclusion: Overall metrics concealed substantial age-related differences in false-negative rates. Screening-model evaluation should routinely report subgroup false-negative rates together with overall calibration before deployment.

Subgroup False-Negative Rates and Overall Calibration in NHANES Chronic Disease Screening

Genomic research and precision medicine increasingly rely on Genome-Wide Association Studies (GWAS) and Polygenic Risk Scores (PRS) to predict disease risk. However, the fundamental datasets that train these models are overwhelmingly composed of individuals of European descent, raising vital concerns about algorithmic bias. This study measures the extent of this systematic overrepresentation of European ancestry in global genetic databases and the risks it poses to diagnostic accuracy for non-European populations. Using a Comparative Statistical Audit and Biomarker Sensitivity Analysis, we evaluated the gene DENND1A, a major susceptibility locus for Polyendocrine Metabolic Ovarian Syndrome (PMOS), formerly known as Polycystic Ovary Syndrome (PCOS). Our analysis reveals a severe gap in the representation of different groups in genetic databases; although certain PMOS-linked genetic markers are highly prevalent in African and South Asian populations, the sequencing data collected for these groups remains vastly smaller than for European cohorts. Consequently, predictive AI algorithms trained on Eurocentric baselines can mathematically misclassify harmless, high-frequency variations in underrepresented groups as abnormal, leading to false positives. To ensure diagnostic AI does not systematically misdiagnose minority populations, achieving equitable clinical outcomes requires a fundamental shift toward ancestral diversity in foundational training data.

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