Artificial Intelligence technologies have the potential to vastly improve our health care services, by improving the efficiency of diagnosis, streamlining the allocation of resources, and identifying new medicines and treatments. But these technologies also risk increasing existing health disparities by discriminating against marginalised groups. There is already evidence of racial bias in AI technologies integrated into the healthcare system, which could deny fatal treatment to, misdiagnose, or design treatments ineffective against minority ethnic, black or brown patients.
Health Monitoring Devices have gained popularity over the past few years, and hold promise in helping people to reach their wellness goals. However, these devices rely on un-representative data-driven algorithms, which leaves ethnic minorities vulnerable to their ineffectiveness.
Modern medicines and treatments can be improved with the progression of AI in medicine by discovering new drugs, personalising treatments, and speeding up chemical trials. But if the racial exclusion common in biomedical research seeps into the data behind AI there’s a risk that these medicines wont be effective for everyone.
AI racial bias has extended directly into vital aspects of patient care. With greater rates of misdiagnosis and underdiagnoses of skin cancers in the non-white populations, this is a prime example of AI systems adding to the global burden of health disparities which significantly impact minority groups.
AI can help to improve resource management within the health care system, by locating gaps in the care system, rebalancing resources, and reviewing patient data to identify priority care patients. But how do we ensure that AI systems distribute resources and care fairly? Several case studies show how bias seeping into AI technologies is underserving black and ethnic minority patients.
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