Objective: As the number of radiology artificial intelligence (AI) papers increases, there are new challenges for reviewing the AI literature as well as differences to be aware of, for those familiar with the clinical radiology literature. We aim to introduce a tool to aid in this process.
Methods: In evidence-based practise (EBP), you must Ask, Search, Appraise, Apply and Evaluate to come to an evidence-based decision. The bottom-up evidence-based radiology (EBR) method allows for a systematic way of choosing the correct radiological investigation or treatment. Just as the population intervention comparison outcome (PICO) method is an established means of asking an answerable question; herein, we introduce the data algorithm training output (DATO) method to complement PICO by considering Data, Algorithm, Training and Output in the use of AI to answer the question.
Results: We illustrate the DATO method with a worked example concerning bone age assessment from skeletal radiographs. After a systematic search, 17 bone age estimation papers (5 of which externally validated their results) were appraised. The paper with the best DATO metrics found that an ensemble model combining uncorrelated, high performing simple models should achieve error rates comparable to human performance.
Conclusion: Considering DATO in the application of EBR to AI is a simple systematic approach to this potentially daunting subject.
Advances in knowledge: The growth of AI in radiology means that radiologists and related professionals now need to be able to review not only clinical radiological literature but also research using AI methods. Considering Data, Algorithm, Training and Output in the application of EBR to AI is a simple systematic approach to this potentially daunting subject.