Health Citizens – European Research Center

Tag: Diabetes

  • Algorithmic Bias in AI-Based Diabetes Care: Systematic Review of Model Performance, Equity Reporting, and Physiological Label Bias

    Algorithmic Bias in AI-Based Diabetes Care: Systematic Review of Model Performance, Equity Reporting, and Physiological Label Bias

    AI has transformed diabetes management, particularly through tools that predict glucose levels and support clinical decisions. However, there are concerns that these technologies may not work equally well for different racial, ethnic and socioeconomic groups. This systematic review assessed how fair AI-based glucose prediction models are, focusing on differences in performance between groups, how fairness is reported, and biases linked to physiological measures.

    Of 1,243 articles initially identified, only 10 met the criteria for inclusion. Just one of them explicitly evaluated whether AI model performance differed by race. Reporting on fairness was rare: only 7% of AI diabetes studies documented ethnic or racial data, and almost none carried out fairness audits. The review also highlights known physiological differences: for the same average glucose level, African American patients tend to have higher HbA1c values than White patients. If HbA1c is used as a reference to train AI models, this difference can be built into them. Yet none of the AI studies reviewed took this into account.

    The authors also note that traditional diabetes risk scores overestimate risk for White individuals and underestimate it for Black individuals, a bias that AI systems trained on these scores could inherit. They conclude that, without systematic efforts to reduce bias, AI-based diabetes technologies risk worsening existing health disparities, and call for future research to prioritise transparency, representativeness and fairness.

    Learn more about this review here: https://doi.org/10.61186/ist.202502.05.04

    Reference:

    Alipour, M., & Alipour, A. (2025). Algorithmic Bias in AI-Based Diabetes Care: Systematic Review of Model Performance, Equity Reporting, and Physiological Label Bias. InfoScience Trends, 2(5), 33–46.

  • Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

    Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

    Machine learning models can help predict which patients with diabetes are likely to be readmitted to hospital, but they need to work fairly for all patients. This study compared several of these models, including deep learning, generalised linear models, gradient boosting machines and naive Bayes, assessing not only their accuracy but also how fair their predictions were across age, gender and race.

    The gradient boosting model performed best overall, with an accuracy of 82.2% and consistently low error rates for most racial and gender groups. However, across several of the models, African American and Hispanic patients had higher rates of false positives, meaning they were more likely to be wrongly predicted to be readmitted. No single model performed best for every group: some worked better for certain groups of patients and others for other groups.

    The authors conclude that there is no one-size-fits-all solution, and that models need to be chosen and adjusted carefully to ensure they are both accurate and fair for all patients. According to them, fair algorithms can help reduce disparities and improve outcomes for patients with diabetes from all backgrounds.

    Learn more about this study here: https://doi.org/10.1109/ICHI61247.2024.00105

    Reference:

    Al-Zanbouri, Z., Sharma, G., & Raza, S. (2024). Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions. In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) (pp. 660–669). IEEE.

  • Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes

    Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes

    Continuous glucose monitors, small sensors worn on the body that track blood sugar levels throughout the day, are increasingly combined with AI models that predict where glucose levels are heading. Because White people are overrepresented in medical research, these models may perform worse for other groups if they are trained mainly on White patients’ data. This study tested this using data from 205 adults and children with type 1 diabetes in the US, 101 White and 104 Black.

    The researchers trained AI models to predict glucose levels 60 minutes ahead, using datasets with different proportions of White and Black participants, from 0% to 100% White. As the proportion of White participants in the training data increased, prediction errors grew slightly more for Black participants than for White participants. The difference was small but statistically significant.

    When the models were then personalised to each individual, using a technique called transfer learning, this difference became smaller and was no longer statistically significant. The authors note that the differences may not be large enough to matter clinically on their own, but conclude that diverse training data is important for fair prediction models, and that personalisation may help build fairer tools.

    Learn more about this study here: https://doi.org/10.1371/journal.pdig.0000918

    Reference:

    Thomsen, H.B., Li, L.Y., Isaksen, A.A., Lebiecka-Johansen, B., Bour, C., Fagherazzi, G., van Doorn, W.P.T.M., Varga, T.V., & Hulman, A. (2025). Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes. PLOS Digital Health, 4(6), e0000918.

  • Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia

    Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia

    Diabetic retinopathy, damage to the retina caused by diabetes, can lead to vision loss if it is not detected early. This study tested an AI system for diabetic retinopathy screening in real-world conditions, in two endocrinology clinics in Melbourne and three Aboriginal medical services in Western Australia, and also gathered the views of patients and health professionals.

    Of the 236 people screened, 203 were included in the main analysis. Overall, the system identified diabetic retinopathy requiring referral with a sensitivity of 96.9% and a specificity of 87.7%, and over 93% of participants were satisfied with the service. However, specificity was substantially lower among participants recruited from Aboriginal medical services, meaning that more people without the condition were wrongly flagged for referral. The study used clinic location as an approximation of Indigenous status.

    The authors note that the AI system had been trained exclusively on images from Chinese adults, and that differences in retinal pigmentation may affect its accuracy. They also point to other possible factors, such as poorer image quality, older age and other eye conditions. These explanations were not tested in the study.

    Learn more about this study here: https://doi.org/10.1038/s41598-021-94178-5

    Reference:

    Scheetz, J., Koca, D., McGuinness, M., Holloway, E., Tan, Z., Zhu, Z., O’Day, R., Sandhu, S., MacIsaac, R.J., Gilfillan, C., Turner, A., Keel, S., & He, M. (2021). Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia. Scientific Reports, 11, 15808.

  • Fairness And Bias Mitigation in AI Models for Diabetes Diagnosis: A Comparative Evaluation of Algorithmic Approaches

    Fairness And Bias Mitigation in AI Models for Diabetes Diagnosis: A Comparative Evaluation of Algorithmic Approaches

    AI models that predict diabetes can produce biased results for certain groups of patients, and several methods have been developed to reduce this. This study compared three of these methods on the same data, a widely used dataset of 768 women aged 21 and over, assessing fairness across age groups.

    Each method had different strengths. The first relied on simple, transparent decision rules, making its predictions easy to interpret, but it had no specific mechanism to ensure fairness. The second was designed to prevent the model from basing its predictions on age, and achieved perfect results on the fairness measures used while keeping accuracy at an acceptable level. The third corrected the imbalance between patients with and without diabetes in the data, which gave the highest overall accuracy but did not ensure fairness.

    The authors conclude that none of the methods achieves the right balance between accuracy, fairness and interpretability on its own, and propose combining several strategies to reduce bias in AI-based diabetes diagnosis.

    Learn more about this study here: https://doi.org/10.54554/jtec.2025.17.03.004

    Reference:

    Mohd Hamdan, M.D.H., Ab Jabal, M.F., Abdul Rahman, S., & Kapi, A.Y. (2025). Fairness And Bias Mitigation in AI Models for Diabetes Diagnosis: A Comparative Evaluation of Algorithmic Approaches. Journal of Telecommunication, Electronic and Computer Engineering, 17(3), 27–34.

  • Implicit gender bias in the diagnosis and treatment of type 2 diabetes: A randomized online study

    Implicit gender bias in the diagnosis and treatment of type 2 diabetes: A randomized online study

    This study examined whether implicit gender biases, unconscious associations about people based on their gender, influence how general practitioners (GPs) diagnose and treat type 2 diabetes. Ninety-nine GPs completed tests measuring two biases, one related to lifestyle (women having a healthier lifestyle than men) and one related to communication (men being less communicative than women), and then assessed clinical cases featuring a male or female patient.

    Female GPs showed a significant lifestyle-related bias, while GPs of both genders showed a communication-related bias. The patient’s gender also affected clinical decisions: for example, GPs were less certain about the diabetes diagnosis when the patient was a woman.

    These findings show how unconscious bias can shape clinical decision-making.

    Learn more about this study here: https://doi.org/10.1111/dme.15087

    Reference:

    Skvortsova, A., Meeuwis, S.H., Vos, R.C., Vos, H.M.M., van Middendorp, H., Veldhuijzen, D.S., & Evers, A.W.M. (2023). Implicit gender bias in the diagnosis and treatment of type 2 diabetes: A randomized online study. Diabetic Medicine, 40(8), e15087