Validation

After an algorithm has been trained, it’s important to test the model to ensure its ‘learned knowledge’ is accurate, complete, and consistently capable of providing a suitable output – be that ‘advice’ (in the form of clinical decision support), classification or prediction. This is a process known as validation. 

This is crucial because it can highlight issues such as bias, overfitting and the resulting poor generalisability, and problems with the overall accuracy or performance of the model such as a tendency to generate excessive false positives. If these problems are not caught early in the development process of an algorithm, once it’s deployed, the algorithm could cause harm to patients through misdiagnosis or missed diagnosis, or undermine scientific endeavours by producing results overly reliant on spurious correlation. 

The validation process

Exactly how a model is validated will vary depending on the type of model, what it’s designed to do, the wider context in which it was designed, and the clinical outcome it’s targeting. However, there are some generally applicable guidelines, as follows: 

1. Validation should involve multiple stages: 
   a.     Internal validation: validation of performance against a subset of the training dataset. 
   b.    Temporal validation: validation from the same clinical centre (e.g., the same hospital) as the training dataset, but from a different time period.  
   c.     External validation: validation of performance against a dataset comprised of patients from an entirely different clinical setting (e.g., a completely different hospital)
2. Validation should include testing for robustness i.e., how well can the model cope with different case mixes of patients, variations in missingness and uncertainty, and variations in instrumentation and care protocols between different settings. 
3. Validation should include benchmarking or the comparison of the performance of the model to either another model, another similar intervention, or to human performance. 
4. Validation should involve a range of statistical performance tests and metrics to identify, in detail, the strengths and weaknesses of the model. Such metrics might include the ROC AUC, mean, absolute mean, mean squared error, rand error, warping error, positive-predictive value, concordance-index, and more. Other metrics used for testing recommender systems may also be used. The exact selection of metric will depend on the type of model (e.g., supervised or unsupervised) and the type of clinical task (e.g., classification or prediction) 
5. Validation should include the transparent and open publication of results in accordance with relevant reporting guidelines e.g., MINIMAR (MINimum Information for Medical AI Reporting) and wherever feasible, all statistical metrics should be reported with confidence intervals or measures of statistical significance. 
6. Validation studies should be conducted by multi-disciplinary teams involving statisticians, clinical informaticians, data scientists, and clinicians (at a minimum). 

In other words, rigorous technical validation requires testing for accuracy, reliability, and robustness. This will ensure that the model’s ‘knowledge’ is accurate and can ‘perform’ well under a variety of different conditions. However, technical accuracy is not the same as clinical efficacy.  

Clinical efficacy can only be determined by testing models in real-life clinical settings i.e. clinical evaluation. This might involve conducting a randomised controlled trial, a cluster trial, observational study, or time series analysis. Generating evidence of efficacy using one of these methods, depending on the level of ‘risk’ associated with the model, is a legal requirement if the model is intended to be used in a medical device (i.e., deployed in a clinical setting and not used just for research purposes).