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.
Authors
- Iris Z. Shen
- Lanjing Zhang (ORCID: https://orcid.org/0000-0001-5436-887X)
Institutions
- Rutgers, The State University of New Jersey (US)
- University Medical Center of Princeton (US)
- Rutgers Cancer Institute (US)
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
Funders
- National Institutes of Health
- National Cancer Institute