Training activity information
Details
Validate and interpret the output of an AI model and present findings to an audience of peers
Type
Developmental training activity (DTA)
Evidence requirements
Evidence the activity has been undertaken by the trainee.
Reflection on the activity at one or more time points after the event including learning from the activity and/or areas of the trainees practice for development.
An action plan to implement learning and/or to address skills or knowledge gaps identified.
Considerations
- Underlying algorithms
- Requirements for test data
- Statistical modelling
- Documentation
- Commercial systems
- Use and interpretation of performance metrics
- Validation techniques, including k-fold cross-validation
- Bias – variance tradeoff, learning curves
- ‘Acceptable’ false positive and false negative rates
- Best practice, sharing knowledge and output reproducibility
Reflective practice guidance
The guidance below is provided to support reflection at different time points, providing you with questions to aid you to reflect for this training activity. They are provided for guidance and should not be considered as a mandatory checklist. Trainees should not be expected to provide answers to each of the guidance questions listed.
Before action
- What do you need to know before undertaking validation and interpretation? This includes understanding model evaluation metrics, techniques for interpreting model outputs, and principles of effective scientific communication.
- What do you anticipate you will learn from this experience? Consider developing skills in critically assessing model performance, deriving meaningful insights from model predictions, and communicating these findings to a technical audience. Reflect on your current knowledge of AI model evaluation and scientific presentation.
- What actions will you take in preparation for this experience? Will you review model evaluation techniques? Will you plan how to interpret the model outputs in the context of the healthcare problem? Will you prepare the structure and content of your presentation? Consider potential challenges in interpreting complex model outputs or explaining findings clearly to peers and how you might address them. Identify how you feel about embarking on this training activity.
In action
- When validating the model, what evaluation metrics are you using and why? How are you assessing the model’s performance on unseen data?
- During interpretation, what techniques are you employing to understand the model’s predictions and insights? What decisions are you making about how to present these findings clearly and concisely to your peers?
- Which aspects of model validation and interpretation feel more straightforward, and where do you need to think critically about the implications of the results and the best way to communicate them?
- How confident are you in the model’s performance based on your validation? What key insights are you able to extract from the model’s output? What challenges are you facing in presenting these findings effectively to your peers?
- What are you learning about the process of evaluating and explaining AI models in a healthcare context? How does this relate to principles of responsible AI development and scientific communication?
- If the validation results are not as expected or you are struggling to interpret the model’s output, what alternative validation techniques or interpretation methods could you use? Would seeking feedback on your presentation from a mentor or colleague be helpful? Are you ensuring that your presentation of the findings is accurate and appropriately contextualised?
On action
- Describe the AI model you validated, the methods you used for validation, how you interpreted its output, and how you presented your findings to your peers.
- What did you learn about the importance and methods of AI model validation? How did you interpret the output of the AI model in a meaningful way for a healthcare context? What did you learn about presenting technical findings to a peer audience? What feedback did you receive from your peers on your interpretation and presentation?
- How can you improve your skills in validating and interpreting AI model outputs? How can you enhance your presentation skills for technical audiences? What are your next steps in learning more about different validation techniques and effective communication strategies? Do you require any further resources on AI model validation or presentation best practices?
Beyond action
- Have you revisited the validation results and your interpretation of the AI model output? How has your ability to critically assess AI model performance and explain its findings evolved? Have you presented AI results to other audiences since?
- How has this activity contributed to your understanding of the importance of explainability and transparency in AI for healthcare in your current practice? Have the communication skills been useful?
- What transferable skills, such as critical evaluation and communication, did you develop? What further learning in AI model validation, interpretation, and presentation techniques would be valuable?
Relevant learning outcomes
| # | Outcome |
|---|---|
| # 10 |
Outcome
Design, develop, train and validate AI models using alphanumeric and imaging datasets. |