AI deepens discrimination in health fields

However, as with all technologies, AI can be applied in ways that are detrimental. The risks associated with medicine and healthcare include the potential for AI errors to cause patient harm,11 12 issues with data privacy and security13–15 and the use of AI in ways that will worsen social and health inequalities by either incorporating existing human biases and patterns of discrimination into automated algorithms or by deploying AI in ways that reinforce social inequalities in access to healthcare.16 One example of harm accentuated by incomplete or biased data was the development of an AI-driven pulse oximeter that overestimated blood oxygen levels in patients with darker skin, resulting in the undertreatment of their hypoxia.17 Facial recognition systems have also been shown to be more likely to misclassify gender in subjects who are darker-skinned.18 It has also been shown that populations who are subject to discrimination are under-represented in datasets underlying AI solutions and may thus be denied the full benefits of AI in healthcare.16 19 20 Although there is some acknowledgement of the risks and potential harms associated with the application of AI in medicine and healthcare,11–16 20 there is still little discussion within the health community about the broader and more upstream social, political, economic and security-related threats posed by AI. With the exception of some voices,9 10 the existing health literature examining the risks posed by AI focuses on those associated with the narrow application of AI in the health sector.11–16 20 This paper seeks to help fill this gap. It describes three threats associated with the potential misuse of narrow AI, before examining the potential existential threat of self-improving general-purpose AI, or artificial general intelligence (AGI) (figure 1). It then calls on the medical and public health community to deepen its understanding about the emerging power and transformational potential of AI and to involve itself in current policy debates on how the risks and threats of AI can be mitigated without losing the potential rewards and benefits of AI.