The problem with the ‘truth’: rethinking ground truth for artificial intelligence in endometriosis diagnosis

Abstract

Artificial intelligence (AI) is revolutionizing how we practice medicine. In areas where we have traditionally struggled, such as diagnosing endometriosis, AI has significant potential to improve the breadth and accuracy of diagnostic services offering a great benefit to patient care. When developing AI models for diagnosis, the 'ground truth' refers to the reference standard used in the labelling of the data used to train the model. Conventionally, in clinical medicine, we correlate any new diagnostic tool to the established 'gold standard', which in the case of endometriosis is laparoscopic visualization of lesions and histological confirmation. This method however is increasingly recognized as imperfect. Acknowledgement of the limitations of surgery and recent improvements in the diagnostic capability of imaging technologies to detect endometriosis, has created a situation where endometriosis no longer has one clear 'gold standard' for diagnosis. In this commentary, we will explore the impact of this on AI-driven endometriosis diagnostic tools and propose novel ways this could be addressed in the context of creating ground truths for endometriosis diagnosis.

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