# AI for pathologists: a universal lymph node metastasis detection app that enhances efficiency while preserving diagnostic accuracy

## TL;DR

This paper introduces an AI app that helps pathologists detect lymph node metastasis across multiple cancer types, improving efficiency without sacrificing accuracy.

## Contribution

A universal AI app for lymph node metastasis detection trained on limited data and applicable to multiple cancer types.

## Key findings

- The AI app detected metastasis in 12 cancer types from 15 organ systems using 172 slides.
- Pathologists reduced search time per slide from 54.7 to 42.1 seconds without losing accuracy.
- The app's annotation maps guided pathologists effectively, enhancing workflow efficiency.

## Abstract

Increasing workload combined with the shortage of pathologists is the leading cause of diagnostic errors and delays. Nonetheless, in clinical practice, pathologists often spend hours on tedious tasks such as counting mitoses and searching for lymph node micro‐metastasis, which may yield unreliable results. The advent of digital pathology and the development of artificial intelligence (AI) applications (app) for image analysis have opened new possibilities for improving the efficiency and accuracy of pathologists. However, the perceived black box nature of AI has led to skepticism among many pathologists about its diagnostic capabilities, resulting in a lack of trust in AI. In addition, it is a common belief that AI applications should be limited to the areas they were trained in, which has significantly limited their generalizability. Given the homogeneous cell population of lymph nodes and overlapping of tumor morphology across different organs, we hypothesized that a lymph node metastasis detection application trained on a few organs could potentially recognize metastasis from multiple organs. We used the commercially available Visiopharm app (AI tool), initially trained on lymph node metastases from breast and colon cancer, to detect metastasis of 12 distinct types of cancer from 15 organ systems based on the analysis of 172 slides (all with corresponding immunohistochemical staining confirmation). Furthermore, by using the annotation map generated by the app as a guide, pathologists were also able to reduce the time spent searching for metastasis substantially (from 54.7 to 42.1 s per slide on average) without compromising diagnostic accuracy. With pathologists serving as the trusted gatekeepers and the development of more sophisticated image analysis applications, the use of AI can help to address the shortage of pathologists, enhance their performance and eventually improve patient care.