on 17 September is ‘medication without harm’. The campaign is focused on ‘traditional’ drugs — chemical compounds — for which regulations and standards have been developed to ensure that they are safe before being given to patients.
In the specific case of AI, regulatory bodies have begun to take steps to guide preclinical development, so that the formal approval process and potential market entry — which require that interventions are proven to be effective as well as safe in patients — can be made more consistent. To demonstrate the potential relevance and safety of AI in the real world, translational AI and machine learning studies are recommended to follow the US Food and Drug Administration ’s guiding principles on.
How adverse events present and are assessed in patients requires rethinking in the context of ‘variable’ iterative interventions. Although AI algorithms approved by the FDA have so far been locked before entering the market, the application of models that continuously learn and develop once exposed to real-world populations has to be regulated differently to ‘static’ interventions.
There is also potential for SaMDs to exacerbate existing health inequalities and systemic biases in the real world, representing risks to patient safety on a large scale. For example, AI-based prediction models of chest X-rays are more likely toif they are members of underserved populations. Training and testing of SaMDs in diverse populations before release is crucial to prevent such outcomes, as well as avoiding the use of socially constructed categories, such as race, in SaMDs.
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