A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?

Abstract

Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality.

Level of evidence: Level V.

Keywords: AI; ML; digitalization; healthcare; performance metrics.

Figure 1

Area under the curve (AUC)–receiver operator curve (ROC) graph, the orange line displays the models true positive rate and false positive rate at various thresholds, the dashed blue line represents an AUC of 0.5, no better than chance.