AI and Bias in Healthcare

screenshot of panel speakers

This guest panel series examines the use of AI in assisting healthcare, with a particular focus on automating tasks, communicating diagnoses and allocating resources. It examines the sources of bias in AI integrated systems and what we can do to eliminate it.

Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19

Journal of the American Medical Informatics Association logo

In terms of infection rate, hospitalisation and mortality, the Covid-19 pandemic presents a disproportionate impact on minorities. Many believe that artificial intelligence could be a solution to guide clinical decision making to overcome this novel disease. However, the rapid dissemination of underdeveloped, and potentially biased models could exacerbate the disparities gap by making race affect […]

The Potential for AI in healthcare

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This report discusses current applications of AI as well as potential future applications of AI in the healthcare system. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities.

Addressing Bias: Artificial Intelligence in Cardiovascular Medicine

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Medical paper which examines the potential of Artificial Intelligence in cardiovascular medicine; it could hugely benefit patient diagnosis and treatment of what is the leading cause of morbidity and mortality worldwide. However, AI algorithms are still subject to their own biases, and predictive models might worsen health disparities through biases in the data training the […]

Debiasing artificial intelligence: Stanford researchers call for efforts to ensure that AI technologies do not exacerbate health care disparities

A medical device being used on a woman of colour

Medical devices utilising AI technologies stand to reduce general biases in the health care system, however, if left unchecked, the technologies could unintentionally perpetuate sex, gender, and race biases. The AI devices rely on data-driven algorithms to inform health care decisions and aid in the diagnosis of diseases. After examining the biases inherent in these […]

Is a racially biased algorithm delaying healthcare for one million black people?

A woman of colour being treated by doctors

An estimated one million black adults would be transferred earlier for kidney disease if US health systems removed a ‘race-based correction factor’ from an algorithm they use to diagnose people and decide whether to administer medication. There is a debate surrounding whether or not race-based correction should be removed, on the one hand, it perpetuates […]

Can we trust AI not to further embed racial bias and prejudice?

illustration of doctors consulting with white patients while black patients are excluded

Journalist Poppy Noor investigates how black people with melanoma are being underserved in healthcare, and the link to the racist algorithms driving new cancer software. Most of these algorithms use non-representative data, trained on majority white skin patients, resulting in black patients not being diagnosed.

AI-Driven Dermatology Could Leave Dark-Skinned Patients Behind

human skin cells

AI technologies have the potential to save thousands of people from skin cancer yearly, by aiding early diagnosis. However, this shift is also potentially dangerous for darker-skinned patients, as the demographic imbalances in dermatology can leave machine learning diagnoses less effective for darker-skinned patients.

How a Popular Medical Device Encodes Racial Bias

A patient using a pulse oximeter

COVID-19 care has brought the pulse oximeter to the home, it’s a medical device that helps to understand your oxygen saturation levels. This article examines research that shows oximetry’s racial bias. Oximeters have been calibrated, tested and developed using light-skinned individuals. For a non-white person, inaccurate readings could be fatal.

Skin Deep: Racial Bias in Wearable Tech

Black woman in sportswear looking at her smartphone with fitness data superimposed across the image

Health monitoring devices influence the way that we eat, sleep, exercise, and perform our daily routines. But what do we do when we discover that the technology we rely on is built on faulty methodology and legacy effects of racial bias?