Digital and Artificial Intelligence-based Pathology: Not for Every Laboratory – A Mini-review on the Benefits and Pitfalls of Its Implementation

We found a generally favorable but cautious outlook for the implementation of DAIP in the pathology workflow. Many studies have reported promising outcomes in using AI for diagnosis and analysis; however, there are also several noteworthy limitations in implementing DAIP. Therefore, a balance between the benefits and pitfalls of DAIP must be thoroughly articulated and examined in light of the institution's needs and goals before making the decision to implement DAIP. Approaches for mitigating machine learning biases were also proposed, and the adaptation and growth of the pathology profession were discussed in light of DAIP development and advances.

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Publication Details

Journal
Journal of Clinical and Translational Pathology
Published
2025-04-03
DOI
https://doi.org/10.14218/jctp.2025.00007
Citations
2
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
4.96

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article

Digital and Artificial Intelligence-based Pathology: Not for Every Laboratory – A Mini-review on the Benefits and Pitfalls of Its Implementation

Iris Z. Shen, Lanjing Zhang
2 citations
Journal of Clinical and Translational Pathology
AI in cancer detection
4.96
article

Digital and Artificial Intelligence-based Pathology: Not for Every Laboratory – A Mini-review on the Benefits and Pitfalls of Its Implementation

Iris Z. Shen, Lanjing Zhang
article en
2 citations

Abstract

We found a generally favorable but cautious outlook for the implementation of DAIP in the pathology workflow. Many studies have reported promising outcomes in using AI for diagnosis and analysis; however, there are also several noteworthy limitations in implementing DAIP. Therefore, a balance between the benefits and pitfalls of DAIP must be thoroughly articulated and examined in light of the institution's needs and goals before making the decision to implement DAIP. Approaches for mitigating machine learning biases were also proposed, and the adaptation and growth of the pathology profession were discussed in light of DAIP development and advances.

Journal of Clinical and Translational PathologyVol. 000(000)
Rutgers, The State University of New Jersey (US), University Medical Center of Princeton (US), Rutgers Cancer Institute (US)
National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 6%
AI in cancer detection
4.96
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