Abstract
The rapidly growing use of artificial intelligence in pathology presents a challenge in terms of study reporting and methodology. The existing guidelines for the design (SPIRIT) and reporting (CONSORT) of clinical trials have been extended with the aim of ensuring production of the highest quality evidence in this field. We explore these new guidelines and their relevance and application to pathology as a specialty. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
Highlights
The word ‘revolution’ is somewhat overused in technology circles, the recent leap in performance of artificial intelligence (AI) systems surely does justify the term
Driven by advances in a particular type of neural network called ‘deep learning’ [1], computers have achieved human-level performance in a number of tasks previously considered to be some decades in the future [1,2,3]
The area of pathological diagnosis has been included in this revolution [4] and arguably pathology data are ideally suited to the application of deep learning, which at its core is a pattern-recognition tool ‘trained’ on data to classify new ‘test’ data
Summary
The word ‘revolution’ is somewhat overused in technology circles, the recent leap in performance of artificial intelligence (AI) systems surely does justify the term. The area of pathological diagnosis has been included in this revolution [4] and arguably pathology data (and image interpretation) are ideally suited to the application of deep learning, which at its core is a pattern-recognition tool ‘trained’ on data to classify new ‘test’ data. In a short period of time, we have seen the technology applied successfully in a variety of applications, with resulting histopathology-focused papers in high impact general medical and science journals [5,6,7,8,9,10,11], many claiming pathologist-level performance.
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