Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification

doi:10.1016/j.modpat.2025.100765

Review

. 2025 Jul;38(7):100765.

doi: 10.1016/j.modpat.2025.100765. Epub 2025 Apr 8.

Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification

Affiliations

¹ Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma. Electronic address: lewis-hassell@ouhsc.edu.
² Department of Pathology, University of Chicago Medical Center, Chicago, Illinois.
³ The Pathology Laboratory, Lake Charles, Louisiana.
⁴ Department of Pathology, Evanston Hospital, Evanston, Illinois.
⁵ Department of Pathology, College of Medicine, University of Florida, Jacksonville, Florida.
⁶ H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
⁷ Warren Alpert School of Medicine, Brown University, Providence, Rhode Island.
⁸ Department of Pathology, Computational Pathology & AI Center of Excellence (CPACE), School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
⁹ Kameda Medical Center, Kamogawa, Chiba, Japan.
¹⁰ International Agency for Research on Cancer, Lyon, France.
¹¹ University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.

PMID: 40204094
PMCID: PMC12272330
DOI: 10.1016/j.modpat.2025.100765

Review

Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification

Lewis A Hassell et al. Mod Pathol. 2025 Jul.

. 2025 Jul;38(7):100765.

doi: 10.1016/j.modpat.2025.100765. Epub 2025 Apr 8.

Authors

Affiliations

¹ Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma. Electronic address: lewis-hassell@ouhsc.edu.
² Department of Pathology, University of Chicago Medical Center, Chicago, Illinois.
³ The Pathology Laboratory, Lake Charles, Louisiana.
⁴ Department of Pathology, Evanston Hospital, Evanston, Illinois.
⁵ Department of Pathology, College of Medicine, University of Florida, Jacksonville, Florida.
⁶ H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
⁷ Warren Alpert School of Medicine, Brown University, Providence, Rhode Island.
⁸ Department of Pathology, Computational Pathology & AI Center of Excellence (CPACE), School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
⁹ Kameda Medical Center, Kamogawa, Chiba, Japan.
¹⁰ International Agency for Research on Cancer, Lyon, France.
¹¹ University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.

PMID: 40204094
PMCID: PMC12272330
DOI: 10.1016/j.modpat.2025.100765

Abstract

The introduction of new diagnostic information in pathology requires effective dissemination and adoption strategies. Although traditional methods like journals, meetings, and atlases have been used, they pose challenges in accessibility, interactivity, and performance validation. Digital pathology (DP) and artificial or augmented intelligence (AI) offer promising solutions to address these limitations. This paper advocates the use of DP and AI tools to facilitate the introduction of new diagnostic information in pathology. It highlights the importance of standardized training and validation sets, digital slide libraries, and AI-enhanced diagnostic tools. Although AI can improve efficiency and accuracy, it is crucial to address potential pitfalls such as over-reliance on AI, bias, and the need for human oversight. By leveraging DP and AI, the efficiency and accuracy of diagnosis, grading, staging, and classification can be augmented, ultimately improving patient care.

Keywords: artificial intelligence; classification systems; competency; diagnosis; digital pathology; grading; training sets; validation.

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Figures

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Key recommendations for optimal use of digital pathology and artificial or augmented intelligence.

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References

1. Metter D.M., Colgan T.J., Leung S.T., Timmons C.F., Park J.Y. Trends in the US and Canadian pathologist workforces from 2007 to 2017. JAMA Netw Open. 2019;2(5) doi: 10.1001/jamanetworkopen.2019.4337. - DOI - PMC - PubMed
1. Cibas E.S., Ali S.Z., NCI Thyroid FNA State of the Science Conference The Bethesda system for reporting thyroid cytopathology. Am J Clin Pathol. 2009;132(5):658–665. doi: 10.1309/AJCPPHLWMI3JV4LA. - DOI - PubMed
1. Vuong H.G., Ngo H.T.T., Bychkov A., et al. Differences in surgical resection rate and risk of malignancy in thyroid cytopathology practice between Western and Asian countries: a systematic review and meta-analysis. Cancer Cytopathol. 2020;128(4):238–249. doi: 10.1002/cncy.22228. - DOI - PubMed
1. Vuong H.G., Chung D.G.B., Ngo L.M., et al. The use of the Bethesda system for reporting thyroid cytopathology in pediatric thyroid nodules: a meta-analysis. Thyroid. 2021;31:1203–1211. doi: 10.1089/thy.2020.0702. - DOI - PubMed
1. Kurian E.M., Dawlett M., Wang J., Gong Y., Guo M. The triage efficacy of fine needle aspiration biopsy for follicular variant of papillary thyroid carcinoma using the Bethesda reporting guidelines. Diagn Cytopathol. 2012;40(Suppl 1):E69–E73. doi: 10.1002/dc.21718. - DOI - PubMed

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[1] Metter D.M., Colgan T.J., Leung S.T., Timmons C.F., Park J.Y. Trends in the US and Canadian pathologist workforces from 2007 to 2017. JAMA Netw Open. 2019;2(5) doi: 10.1001/jamanetworkopen.2019.4337. - DOI - PMC - PubMed

[2] Metter D.M., Colgan T.J., Leung S.T., Timmons C.F., Park J.Y. Trends in the US and Canadian pathologist workforces from 2007 to 2017. JAMA Netw Open. 2019;2(5) doi: 10.1001/jamanetworkopen.2019.4337. - DOI - PMC - PubMed

[3] Cibas E.S., Ali S.Z., NCI Thyroid FNA State of the Science Conference The Bethesda system for reporting thyroid cytopathology. Am J Clin Pathol. 2009;132(5):658–665. doi: 10.1309/AJCPPHLWMI3JV4LA. - DOI - PubMed

[4] Cibas E.S., Ali S.Z., NCI Thyroid FNA State of the Science Conference The Bethesda system for reporting thyroid cytopathology. Am J Clin Pathol. 2009;132(5):658–665. doi: 10.1309/AJCPPHLWMI3JV4LA. - DOI - PubMed

[5] Vuong H.G., Ngo H.T.T., Bychkov A., et al. Differences in surgical resection rate and risk of malignancy in thyroid cytopathology practice between Western and Asian countries: a systematic review and meta-analysis. Cancer Cytopathol. 2020;128(4):238–249. doi: 10.1002/cncy.22228. - DOI - PubMed

[6] Vuong H.G., Ngo H.T.T., Bychkov A., et al. Differences in surgical resection rate and risk of malignancy in thyroid cytopathology practice between Western and Asian countries: a systematic review and meta-analysis. Cancer Cytopathol. 2020;128(4):238–249. doi: 10.1002/cncy.22228. - DOI - PubMed

[7] Vuong H.G., Chung D.G.B., Ngo L.M., et al. The use of the Bethesda system for reporting thyroid cytopathology in pediatric thyroid nodules: a meta-analysis. Thyroid. 2021;31:1203–1211. doi: 10.1089/thy.2020.0702. - DOI - PubMed

[8] Vuong H.G., Chung D.G.B., Ngo L.M., et al. The use of the Bethesda system for reporting thyroid cytopathology in pediatric thyroid nodules: a meta-analysis. Thyroid. 2021;31:1203–1211. doi: 10.1089/thy.2020.0702. - DOI - PubMed

[9] Kurian E.M., Dawlett M., Wang J., Gong Y., Guo M. The triage efficacy of fine needle aspiration biopsy for follicular variant of papillary thyroid carcinoma using the Bethesda reporting guidelines. Diagn Cytopathol. 2012;40(Suppl 1):E69–E73. doi: 10.1002/dc.21718. - DOI - PubMed

[10] Kurian E.M., Dawlett M., Wang J., Gong Y., Guo M. The triage efficacy of fine needle aspiration biopsy for follicular variant of papillary thyroid carcinoma using the Bethesda reporting guidelines. Diagn Cytopathol. 2012;40(Suppl 1):E69–E73. doi: 10.1002/dc.21718. - DOI - PubMed

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Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification

Affiliations

Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification

Authors

Affiliations

Abstract

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References

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