Healthcare Data Analytics Technology • 2 min read

How can artificial intelligence mitigate bias in healthcare?


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. 

Bias in healthcare exists

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 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. 

How to mitigate bias in healthcare with AI

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 minimising the potential for mistakes to be made – avoiding unconscious bias in the process.

Tackling ageism with AI for unbiased user experience

It’s important to recognise that if AI maturity isn’t properly monitored, AI technologies can easily replicate biases already present in our society, potentially exacerbating previously existing disparities in care. Ensuring the user experience is of a high quality for all age groups is a must. Older people are at risk of being excluded from data sets used to train AI tools, and it’s not uncommon for this demographic to be overlooked or neglected throughout the design and testing phases of market research. Building frameworks to include and empower age-diverse design, data science and collection teams might be one potential solution, as individuals falling within this demographic themselves could be better able to recognise some of the hurdles faced by this particular group when it comes to UI and UX design.

Bias in healthcare: steep learning curve ahead 

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. Increased research into AI and the ethics surrounding it will help us better understand how it works and address any issues before they appear, benefiting not just the healthcare sphere, but a wide variety of industries set to implement AI-based technologies. De-biasing 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 recognise, analyse 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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