Human–machine interaction in computational cancer pathology

The performance of the augmented pathologist that works in synergy with artificial intelligence (AI) is generally accepted as the most accurate in comparison to AI standing alone and the general pathologist standing alone. Human-machine interactions triggered by the synergic daily work give rise to trust-related concerns and potential biases that need to be addressed. The long-term use of AI requires actions to prevent deskilling of the pathology workforce, and to ensuring appropriate education of future generations. Establishment of clear guidelines for the verification and validation of AI tools is crucial for the maintenance of high-quality cancer pathology.

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Publication Details

Journal
ESMO Real World Data and Digital Oncology
Published
2024-08-13
DOI
https://doi.org/10.1016/j.esmorw.2024.100062
Citations
1
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.35
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article

Human–machine interaction in computational cancer pathology

Antonia Syrnioti, Catarina Eloy, António Polónia, João Correia Pinto
1 citations
ESMO Real World Data and Digital Oncology
AI in cancer detection
0.35
article

Human–machine interaction in computational cancer pathology

Antonia Syrnioti, Catarina Eloy, António Polónia, João Correia Pinto
article en
1 citations

Abstract

The performance of the augmented pathologist that works in synergy with artificial intelligence (AI) is generally accepted as the most accurate in comparison to AI standing alone and the general pathologist standing alone. Human-machine interactions triggered by the synergic daily work give rise to trust-related concerns and potential biases that need to be addressed. The long-term use of AI requires actions to prevent deskilling of the pathology workforce, and to ensuring appropriate education of future generations. Establishment of clear guidelines for the verification and validation of AI tools is crucial for the maintenance of high-quality cancer pathology.

ESMO Real World Data and Digital OncologyVol. 5
Fernando Pessoa University (PT), Universidade do Porto (PT), Aristotle University of Thessaloniki (GR)
Openalex Percentile: Top 35%
AI in cancer detection
0.35
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Human–machine interaction in computational cancer pathology — Antonia Syrnioti, Catarina Eloy, et al. · ESMO Real World Data and Digital Oncology (2024) | TGRS Research Map | TGRS