Planning and preparing for artificial intelligence implementation

Learning outcomes:

• What is in the Discovery phase and what you need to consider including
  • your current data state
  • decide whether to build, buy or collaborate
  • allocate responsibility for AI, assess your existing data
  • build your AI team
  • get your data ready for AI
  • plan your AI modelling phase.
• What is in the Alpha phase and what you need to consider when plan and prototype your AI model build and service
  • Split the data.
  • Create a baseline model.
  • Build a prototype of the model and service.
  • Test the model and service.
  • Evaluate the model.
  • Assess and refine performance.
• What is the Beta phase and what you need to consider when deploying and maintaining your model.
  • Integrating your model - performance-test the model with live data and integrate it within the decision-making workflow. Integration can happen in a number of ways, from a local deployment to the creation of a custom application for staff or customers. This decision is dependent on your infrastructure and user requirements.
  • Evaluating your model - undertake continuous evaluation to make sure the model still meets business objectives and the model is performing at the level required. This will make sure the model performance is in line with the modelling phase and to help you identify when to retrain the model.

• • Helping users - make sure users feel confident in using, interpreting and challenging any outputs or insights generated by the model

You shouldconsider AI ethics and safety throughout all phases.

This learning maps to capability ID 6.6.3.3.94 of the domain 6 ethical, legal and regulatoryof the Artificial Intelligence (AI) and Digital Healthcare Technologies Capability Framework