How can artificial intelligence mitigate bias in healthcare?

The COVID-19 pandemic has laid bare some of the ongoing problems within the healthcare sector that require further scrutiny – and medical bias is one of the most prominent. In this blog post, we’ll explore the solutions artificial intelligence offers to reduce issues of bias, highlighting some of the ways in which this new approach to care is shaking up the sector and improving the health equity landscape.

One of the biggest pitfalls of traditional approaches to medicine is obvious: human bias. Intentional or not, there’s no denying that human beings have a tendency to be held back by not only their internalized assumptions, but also their inability to organize and process the substantial amounts of information needed to make certain healthcare decisions. There’s extensive data to suggest that unconscious biases can lead to differential treatment of patients – for example, by gender, weight, age, racial background, residential location, and so on. 

Research into this phenomenon argues that bias impacts both healthcare providers and patients in ways that could perpetuate health equity disparities – something brought to the forefront of the healthcare conversation in recent years given the widely differing responses to the ongoing COVID-19 pandemic. Although there is evidence to suggest that changes are being made within this field – through equity-targeted feedback sessions, for example – it goes without saying that there’s plenty of room for improvement. 

Fortunately, artificial intelligence can reduce this unconscious bias in healthcare by filtering certain aspects that might be liable to human error out of the decision-making process. For example, when a physician’s cognitive capacity is low – perhaps due to a variety of factors, such as long working hours, time pressures or a generally stressful working environment – data argues that their memory is biased toward information that is consistent with stereotypes. By removing the need for the clinician to make the actual decision themselves, AI technologies are minimizing the potential for mistakes to be made – avoiding unconscious bias in the process.

As with any new technology and its widespread implementation, there’s a substantial learning curve that’s part and parcel of the experience. The good news is that machine learning models routinely used in both research and application have the potential to address bias in healthcare AI. Debiasing methods are by no means perfect, but evidence and practical implementation suggests that they’re a positive step in the right direction in terms of creating a better landscape of healthcare across society.

Real-life healthcare scenarios are highly complex and require great levels of attention to detail that human actors might not be able to provide 100% of the time – especially in high-stress situations. Using AI models to recognize, analyze and reduce potential bias could be applied to many clinical prediction models before deployment across the healthcare sector – helping practitioners to harness the power of machine learning methods to make a fairer, more effective and more equitable healthcare sector.

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