Shaswath Ganapathi

Abstract Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access.

Challenges of working with AI The system-level barriers to developing and implementing AI that were discussed earlier [19–22] had personally impacted doctors to varying degrees due to the remit of their roles. These included: lack of a regulatory standard; access to patient data; NHS interoperability and IT infrastructure; and difficulty for tech companies (particularly SMEs) in establishing partnerships with health providers due their limited capacity to provide evidence.

To the Editor — As of June 2022, a wide range of Artificial Intelligence (AI) as a Medical Device (AIaMDs) have received regulatory clearance internationally, with at least 343 devices cleared by the US Food and Drug Administration (FDA)1. Despite the enormous potential of AIaMDs, their rapid growth in healthcare has been accompanied by concerns that AI models may learn biases engrained in medical practice and exacerbate health inequalities.