Understanding Clinical Impact of AI Errors in Histopathology: Beyond Technical Performance Metrics
This perspective argues that current AI validation in histopathology relies too heavily on technical metrics like sensitivity and specificity, which fail to capture the real-world clinical impact of misclassifications on patient diagnosis, management and prognosis. The authors advocate for error assessment frameworks modelled on those used for human pathologist errors, and discuss study design and regulatory approaches to keep patient safety at the forefront of AI deployment.
The original study
Understanding the errors made by artificial intelligence algorithms in histopathology in terms of patient impact.
- Authors
- Evans H, Snead D
- Journal
- NPJ digital medicine
- Type
- Journal Article, Review
- PMID
- 38600151
Original abstract
An increasing number of artificial intelligence (AI) tools are moving towards the clinical realm in histopathology and across medicine. The introduction of such tools will bring several benefits to diagnostic specialities, namely increased diagnostic accuracy and efficiency, however, as no AI tool is infallible, their use will inevitably introduce novel errors. These errors made by AI tools are, most fundamentally, misclassifications made by a computational algorithm. Understanding of how these translate into clinical impact on patients is often lacking, meaning true reporting of AI tool safety is incomplete. In this Perspective we consider AI diagnostic tools in histopathology, which are predominantly assessed in terms of technical performance metrics such as sensitivity, specificity and area under the receiver operating characteristic curve. Although these metrics are essential and allow tool comparison, they alone give an incomplete picture of how an AI tool's errors could impact a patient's diagnosis, management and prognosis. We instead suggest assessing and reporting AI tool errors from a pathological and clinical stance, demonstrating how this is done in studies on human pathologist errors, and giving examples where available from pathology and radiology. Although this seems a significant task, we discuss ways to move towards this approach in terms of study design, guidelines and regulation. This Perspective seeks to initiate broader consideration of the assessment of AI tool errors in histopathology and across diagnostic specialities, in an attempt to keep patient safety at the forefront of AI tool development and facilitate safe clinical deployment.