Table 1.
Top 10 must-knows for clinicians using AI models
| 1. Objective & Scope |
|---|
| Purpose: Model’s primary goal (e.g., prediction, diagnosis, recommendation) |
| Target population: The patient demographic the model caters to |
| 2. Model insights |
| Structure: A concise description of the model's design |
| Explainability: Clarity of the model outputs for clinicians and patients |
| Key variables: Main features the model use, and their medical relevance |
| 3. Data source |
| Data origin: Where training and validation data comes from, ensuring relevance to clinician's patient base |
| Adaptability: Ability to retrain the model using local datasets |
| Open access: Accessibility to data/code for replication (e.g., on platforms like GitHub) |
| 4. Evaluation & Validation |
| Performance metrics: Measures of model accuracy |
| Benchmarking: Comparison to simpler, more interpretable models |
| Practical validation: Testing in real clinical settings, beyond just retrospective data |
| 5. Model limitations |
| Performance concerns: Situations or conditions where model efficacy may diminish |
| Reliability: Model’s expression of confidence and uncertainty in its results |
| Error management: Approaches for handling and correcting inaccurate outputs |
| 6. Clinical integration |
| Human oversight: Human involvement in model-driven decisions |
| Workflow integration: Model's fit into existing clinical processes |
| User experience: Interface design and clarity of information |
| Training & education: learning resources provided for staff and clinicians |
| 7. Ethical considerations |
| Demographic equity: Performance consistency across diverse patient groups |
| Fairness audit: Efforts to identify and rectify potential biases |
| 8. Regulatory aspects |
| Data privacy & security: Protocols for patient data management and protection |
| Legal adherence: Compliance with regulations like GDPR, AI Act |
| Clinician liability: Responsibilities when using the model |
| 9. Maintenance & Audit |
| Safety checks: Monitoring model safety and efficiency |
| Updates & evolution: Keeping the model current line with new data and insights |
| 10. Feedback & Reporting |
| Feedback channels: Systems for collecting and addressing user feedback |
| Adverse event: Procedures to handle and report any negative outcomes associated with the model's deployment |