Table 1.

Summary of the AI considerations framework

ConsiderationDescriptor questions
ContextWhat is the reason(s) for using AI in this context? How was the problem addressed previously? What value does AI add here?
DataWhat data do we need to access to train the algorithm? Is it representative of the intended population?
ValidationIs the algorithm valid across geographies and over time? Is it generalisable across populations?
ImplementationHow will the model work ‘in practice’? What are the scope and limitations of using this algorithm? What training is needed for end users?
SurveillanceHow is the performance of the algorithm monitored over time? How can users report errors with model performance?
Success metricsIs the algorithm working as intended? Are we confident the benefits outweigh the costs (eg clinical, health economic and planetary)?
Ethics and governanceWhat safeguards are in place to protect against algorithmic bias? Is the algorithm transparent and explainable?
Managing changeWhat else needs to occur alongside introducing a new algorithm to achieve system-wide improvement?