Canada

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.

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

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

cardiovascular disease

machine-learning

bias

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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.

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

Medical image anonymization is widely used to protect patient privacy, yet its impact on the fairness of artificial intelligence (AI) systems remains poorly understood. This study evaluated whether common anonymization techniques introduce demographic disparities in chest X-ray classification. A DenseNet-121 classifier was fine-tuned on a 1,622-image subset of the CheXpert Plus dataset to detect five thoracic findings: atelectasis, cardiomegaly, consolidation, edema, and pleural effusion. Three training regimes were evaluated using original images, Gaussian-blurred images (7×7 kernel), and border-cropped images (15% border removal). Fairness was assessed using the Equal Accuracy Gap and Equal Opportunity Gap across sex, age group, race, and ethnicity. Both anonymization methods increased fairness disparities relative to the baseline model. Cropping produced the greatest deterioration in equal-opportunity fairness, increasing the mean Equal Opportunity Gap from 0.315 to 0.504, with the largest effects observed across age and ethnicity groups. Blurring produced the greatest increase in equal-accuracy disparities, increasing the mean Equal Accuracy Gap from 0.119 to 0.268, driven primarily by age-group differences. Although race-based results were influenced by very small subgroup sample sizes, the overall pattern was consistent across demographic axes. These findings suggest that privacy-preserving image transformations can unintentionally worsen demographic disparities in medical AI systems. Consequently, anonymization techniques should be evaluated not only for their effectiveness in protecting patient privacy but also for their impact on fairness across patient populations.

Effects of Medical Image Anonymization on Fairness in Chest X-ray Classification

Missing data can create significant problems in healthcare datasets. It can affect interpretations made from the datasets as well as the fairness and performance of models trained on the datasets. While there are approaches that can help correct missing values in a dataset, it can be difficult to identify the mechanism of missingness. In this paper, we design a tool that uses Little’s MCAR test and a shadow matrix correlation analysis to identify whether missingness is consistent with the Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR) mechanisms. Little’s MCAR test was used as the initial screening tool, and the shadow matrix correlation analysis was used to test for relationships between missingness and the observed variables in the dataset. In order to evaluate the framework, synthetic MCAR, MAR, and MNAR scenarios were introduced into a Diet Recommendations dataset, then the tool was applied to the dataset to confirm its ability to detect missingness. The tool was able to successfully flag simulated MCAR and MAR patterns. Due to its nature, MNAR cannot be definitively proven as it depends on unobserved data. However, with a combination of graphical outputs from our tool and relevant domain knowledge, MNAR patterns may be detectable.

Developing a diagnostic tool for assessing missingness mechanisms in healthcare datasets

Artificial Intelligence (AI) is a tool that is increasingly being used in dermatology. However, systems are not always producing the same results in differing skin tones. Inequitable performance between groups can worsen health inequities. This study analyzes whether AI diagnostic perfor- mances differ between Fitzpatrick skin type groups for classifying malignant and non-malignant skin conditions using the deep-learning model MobileNetV2. The data set used was Fitzpatrick17k, which includes 16,577 pictures of skin conditions, labeled by diagnosis and Fitzpatrick skin tone classification (I-VI). Performance was measured through accuracy, precision, recall, and subgroup comparison between skin tone groups. The Fitzpatrick17k dataset was found to be unbalanced with a 2.2 times higher rates of lighter skin tone images (Fitzpatrick I-III) than darker skin tone images (Fitzpatrick IV-VI). Sensitivity analysis across multiple thresholds showed that recall rates were also consistently lower in IV-VI images than in I-III images. These results suggest that the imbalance of data sets can contribute to unfair diagnostic performance and emphasize the need for more representative dermatology datasets.

Assessment of Diagnostic Performance Across Fitzpatrick Skin

Artificial intelligence is increasingly used to predict new drug treatments by learning from large biomedical databases. If those databases describe some diseases far better than others, AI predictions may inherit that imbalance while appearing equally reliable for all. We tested whether this occurs for neglected tropical diseases (NTDs), a group of conditions that affect over a billion people in low-income regions yet receive minimal research investment. Using Open Targets, a widely used drug–disease evidence platform, we trained a knowledge graph model on 759,217 evidence links and audited its predictions across 96 diseases, introducing the clinical translation ratio: the share of a disease's evidence drawn from clinically actionable sources rather than basic research. NTDs had a 42-fold lower clinical translation ratio than other diseases (0.5% versus 20.4%, p = 3.49 × 10⁻⁸). The gap held across four evidence-classification schemes, persisted against matched infectious diseases, and widened over three database releases. Twelve of nineteen NTDs had no clinically actionable evidence in any release. Most concerning, the model did not account for this scarcity. It was equally confident in its NTD predictions despite near-zero supporting evidence, and its top NTD predictions were three times less likely to correspond to known treatments. The model inherits the evidence gap and hides it behind uniform confidence. To address this, we built an open evidence-equity auditing framework and dashboard that reveals when AI confidence rests on evidence poverty. Before AI can accelerate drug discovery, it must first show whose evidence it is missing.

AI Drug Discovery Inherits Evidence Inequity for Neglected Tropical Diseases

There has been an increase in the usage of machine-learning models to analyze cancer transcriptomic data. However, imbalanced datasets can make models appear reliable while underperforming on underrepresented cell populations. This manuscript investigated false inferences when classifying malignant versus non-malignant tumor microenvironment (TME) observations based on class imbalance in a breast cancer Slide-seqV2 spatial transcriptomic dataset. An .h5ad dataset containing 5,833 observations and 12,630 gene-expression features was analyzed in Python using Google Colab. Binary labels were created from cell-type annotations with malignant and non-malignant TME. Results revealed the dataset was highly imbalanced, with malignant observations representing 94.77% of observations. After preprocessing raw counts from adata.raw.X, principal component analysis, a majority-baseline model, logistic regression, confusion matrices, balanced accuracy, minority-class recall, and feature-importance analysis were performed to evaluate model behavior. The majority-class baseline achieved 94.79% accuracy but failed to identify any non-malignant TME observations. Logistic regression achieved 91.30% accuracy and improved balance accuracy to 64.32% but still misclassified 50 of 76 non-malignant TME observations as malignant. Thus, although the majority-class baseline achieved higher overall accuracy, it provided no minority-class detection, whereas logistic regression showed improved performance across both classes. These results show that overall, accuracy can create misleading confidence when evaluating classification performance in this highly imbalanced spatial transcriptomic dataset. The findings illustrate the importance of using imbalance-sensitive evaluation metrics when assessing machine-learning performance in spatial transcriptomic classification.

Class Imbalance and False Inference in Breast Cancer Spatial Transcriptomic Classification

Overlap-based drug and compound prioritization pipelines are attractive because they are scalable, transparent, and easy to apply across large drug-disease search spaces. However, they are also vulnerable to annotation-breadth effects, whereby broadly annotated genes can inflate apparent support across many disease contexts. We developed a bias-aware computational framework for candidate prioritization in Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis (ALS). The framework combines a normalized raw overlap score, annotation-breadth correction using gene-specificity weights, and a matched-null permutation layer applied to a selected high-priority subset. Across 62,887 overlap-positive pairs, correction substantially changed rank order and reduced the contribution of broadly annotated genes. However, exploratory downstream alignment with ClinicalTrials.gov remained weak, and a small curated therapeutic-history benchmark showed mixed recovery of known positives. These findings support the framework as a transparent hypothesis-generation and triage tool, but not as a predictor of clinical efficacy.

A Bias-Aware Computational Framework for Drug and Compound Prioritization across Neurodegenerative Diseases

Clinical artificial intelligence models trained on imbalanced health data systematically underperform for minority subpopulations, raising concerns about whether synthetic data genuinely improve fairness or merely hide underlying bias. This study investigates whether synthetic data augmentation via the Synthetic Minority Oversampling Technique (SMOTE) and Bayesian hierarchical modelling produce meaningful fairness improvements in a diabetes prediction task using the National Health and Nutrition Examination Survey (NHANES) 2019–2020 and 2021–2023 cycles. Three models are developed: a logistic regression baseline trained on imbalanced data, a SMOTE- augmented logistic regression, and a Bayesian hierarchical GLM with per-race intercepts estimated via MCMC sampling. All models are assessed on AUC, F1, precision, and recall stratified by race/ethnicity and sex, with SHAP explainability analyses applied to both logistic regression models and posterior convergence diagnostics reported for the Bayesian model. The baseline model achieves an overall AUC of 0.897 but a recall of 0.524. SMOTE increases recall across all subgroups (overall recall 0.749) but produces asymmetric precision losses and fails to improve AUC, suggesting the model is more willing to predict the positive class, rather than being more accurate. The Bayesian hierarchical model achieves the most balanced precision- recall tradeoff for several subgroups, while posterior diagnostics confirm well-converged chains and modest but non-negligible between-group variance (sigma_alpha ≈ 0.2). This suggests that SMOTE improves sensitivity at the cost of precision without reducing subgroup discrimination gaps, while Bayesian hierarchical modelling helps by estimating uncertainty and capturing explicit group-level differences. Subgroup- stratified evaluation is essential for honest clinical AI assessment.

Does Synthetic Data Fix or Hide Bias?

Breast cancer detection relies heavily on mammographic imaging, but dense tissue can make malignant lesions harder to identify as both dense tissue and tumours appear radiographically similar. With increasing use of AI-based diagnostic tools in medical imaging, it is important to determine whether these models are affected by the same limitations that challenge human interpretation. The purpose of this research is to emphasize the need to train AI models on a more representative dataset that includes high-noise samples to ensure equitable care across demographics. This study investigates whether variability in breast tissue density acts as a confounding factor in AI-driven tumour detection thus leading to diagnostic inaccuracies. Mammographic images from the CBIS-DDSM dataset were analyzed using Mammo-CLIP, a pretrained model for mammography-based classification. Cases were grouped by BI-RADS breast density categories, and model performance was evaluated using sensitivity, specificity, accuracy, FNR, and chi-squared testing between fatty and dense breast tissue groups. Model sensitivity decreased from 64% to 34% and FNR increased from 36.1% to 66.1% with increasing breast density. Classification accuracy was significantly higher in fatty tissue than dense tissue, and cancer detection amongst malignant cases was also significantly reduced in dense tissue. These findings suggest that denser tissue negatively influences Mammo-CLIP’s ability to detect malignant lesions and increases likelihood of missed cancers. This has important clinical implications, as false negatives may delay diagnosis and treatment, particularly for patients with dense breast tissue.

Effect of breast tissue density variability on AI-based tumor detection

Healthcare systems generate vast quantities of clinical data, yet much of this information remains locked within unstructured free-text records, including physician notes and doctor-patient transcripts, making it inaccessible for large-scale analysis. Manually extracting structured information from this data is time-consuming, expensive, and error-prone. Large language models (LLMs) have demonstrated promising performance in a variety of medical fields, including radiology report summary, doctor-patient dialogue transcription, clinical question answering, and diagnostic reasoning. This study evaluates four LLMs (GPT-5.4 mini, Qwen3 32B, Llama 3.1 8B, and Gemini 3.1 Flash-Lite Preview) on their ability to label radiology diagnostic conclusions and clinical assessment summaries from unstructured medical text using MedVAL-Bench, a 2025 Stanford MIMI benchmark with physician-validated reference outputs. The models were tested on two tasks: doctor-patient dialogue summarization (dialogue2note, n=85) and radiology report impression extraction (report2impression, n=190), which were assessed using ROUGE-1, BERTScore F1, and BLEU. The results suggest that proprietary models like GPT-5.4 mini outperformed all models with a ROUGE-1 of 0.376 and BERTScore F1 of 0.860, while Qwen3 32B, Llama 3.1 8B, and Gemini 3.1 Flash-Lite Preview produced comparable results. ROUGE-1 and BLEU scores were consistently low across all models, reflecting the limitations of n-gram metrics in clinical contexts, where a single-word difference can carry critical medical significance. BERTScore F1 emerged as the most reliable indicator of semantic similarity. These findings suggest LLMs can significantly automate clinical text labelling, offering a scalable, low-cost alternative to manual data extraction in healthcare research.

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