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1. Algorithmic Bias in Health Care Exacerbates Social | Executive …
Link: https://www.hsph.harvard.edu/ecpe/how-to-prevent-algorithmic-bias-in-health-care/
Description: WEBMar 12, 2021 · How Does Algorithmic Bias in Health Care Happen — and Why Is It so Damaging to Patients? Algorithmic bias is not a new problem and is not specific to AI. In fact, an algorithm is merely a series of steps—a recipe and an exercise plan are as much of an algorithm as a complex model.
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2. Algorithm Bias and Racial and Ethnic Disparities in Health and Health Care
Link: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2812958
Description: WEBDec 15, 2023 · Equity challenges for artificial intelligence algorithms in health care. Chest. 2022;161 (5):1343-1346. doi: 10.1016/j.chest.2022.01.009 PubMed. Marchesini K, Smith J, Everson J. Increasing the transparency and trustworthiness of …
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3. Artificial intelligence and algorithmic bias: implications for health
Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6875681/
Description: WEBNov 24, 2019 · We define algorithmic bias in health systems, a risk inherent in AI, explore its implications for health systems and identify ways to mitigate it. If bias exists in society it will both manifest in health systems and be represented in algorithms.
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4. Algorithmic bias in health care: Opportunities for nurses to …
Link: https://www.nursingoutlook.org/article/S0029-6554(22)00174-9/fulltext
Description: WEBdefined algorithmic bias as: “ the instances when the application of an algorithm compounds existing inequities in socioeconomic status, race, ethnic background, religion, gender, disability or sexual orientation to amplify them and adversely impact inequities in health systems.
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5. Exploring algorithmic bias and its impact on health research …
Link: https://www.ohsu.edu/sites/default/files/2023-04/Algorithmic%20Bias%20Materials.pdf
Description: WEBExploring algorithmic bias and its impact on health research and clinical practice. March 24th, 2023; 12:00-1:00pm Pacific. Pre-Event Materials. To be prepared to fully participate in this workshop, we strongly advise participants to: 1. Listen to this ~10 minutes recorded Brookings talk section on algorithms that begins at the 10:37 minute mark.
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6. Bias in AI-based models for medical applications: challenges and
Link: https://www.nature.com/articles/s41746-023-00858-z
Description: WEBJun 14, 2023 · The issue of bias being exhibited, perpetuated, or even amplified by AI algorithms is an increasing concern within healthcare. Bias is usually defined as a difference in performance between...
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7. Dissecting racial bias in an algorithm used to manage the health …
Link: https://www.science.org/doi/10.1126/science.aax2342
Description: WEBOct 25, 2019 · Bias occurs because the algorithm uses health costs as a proxy for health needs. Less money is spent on Black patients who have the same level of need, and the algorithm thus falsely concludes that Black patients are …
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8. Practical, epistemic and normative implications of algorithmic …
Link: https://jme.bmj.com/content/medethics/early/2023/02/22/jme-2022-108850.full.pdf
Description: WEBFeb 22, 2023 · ABSTRACT. Background. There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under- represented and marginalised groups (eg, based on gender or race).
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9. Sources of bias in artificial intelligence that perpetuate healthcare
Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931338/
Description: WEBMar 31, 2022 · While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions.
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10. Algorithmic Individual Fairness and Healthcare: A Scoping …
Link: https://www.medrxiv.org/content/10.1101/2024.03.25.24304853.full.pdf
Description: WEBMar 25, 2024 · Objective. Statistical and artificial intelligence algorithms are increasingly being developed for use in health-care. These algorithms may reflect biases that magnify disparities in clinical care, and there is a growing need for understanding how algorithmic biases can be mitigated in pursuit of algorith-mic fairness.
