# Artificial intelligence for pathology—how, where, and why?

**Authors:** Peter Schüffler, Katja Steiger, Carolin Mogler

PMC · DOI: 10.1007/s00292-024-01314-9 · Pathologie (Heidelberg, Germany) · 2024-03-12

## TL;DR

This article reviews the current state and challenges of using artificial intelligence in pathology.

## Contribution

The paper provides an overview of achievements and remaining challenges in AI for pathology.

## Key findings

- Artificial intelligence offers potential advancements in pathology.
- Current algorithms have achieved some goals but face ongoing challenges.

## Abstract

Künstliche Intelligenz verspricht viele Erneuerungen und Erleichterungen in der Pathologie, wirft jedoch ebenso viele Fragen und Ungewissheiten auf. In diesem Artikel geben wir eine kurze Übersicht über den aktuellen Stand, die bereits erreichten Ziele vorhandener Algorithmen und immer noch ausstehende Herausforderungen.

## Full-text entities

- **Genes:** EREG (epiregulin) [NCBI Gene 2069] {aka EPR, ER, Ep}, PGR (progesterone receptor) [NCBI Gene 5241] {aka NR3C3, PR}, ERBB2 (erb-b2 receptor tyrosine kinase 2) [NCBI Gene 2064] {aka CD340, HER-2, HER-2/neu, HER2, MLN 19, MLN-19}
- **Diseases:** Man (MESH:D016750), Tumors (MESH:D009369)
- **Chemicals:** Paraffin (MESH:D010232), Einscannen (-), ER (MESH:D004871)

## Full text

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## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11045628/full.md

## References

28 references — full list in the complete paper: https://tomesphere.com/paper/PMC11045628/full.md

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Source: https://tomesphere.com/paper/PMC11045628