Clinician Perspectives on Digital and Computational Pathology: Clinical Benefits, Concerns, and Willingness to Adopt
Abstract
1. Introduction
2. Materials and Methods
2.1. Survey Methodology
Digital pathology encompasses the acquisition, management, analysis, and interpretation of pathology information generated from digitized glass-slide images.
Computational pathology is a subset of digital pathology that uses AI-guided computer-based quantification and classification of tissue morphology to generate more precise diagnoses and expand the recognition of pathologic changes beyond the limits of conventional visual assessment. This could include automatic detection, localization, or quantification of histological parameters and structural changes.
2.2. Interviews
2.3. Study Participant Selection Criteria
3. Results
3.1. Clinician Awareness, Utilization, and General Comfort with DP/CP
3.2. Benefits and Barriers to DP/CP Adoption
3.3. Clinicians’ Role in DP/CP Adoption
3.4. Willingness to Adopt a Theoretical CP-Based CDx Test
Assume there is a companion diagnostic for a newly approved targeted therapy in lung cancer. The companion diagnostic test uses computational pathology to precisely quantify the level of a biomarker in tumor cell slide images (i.e., quantifying a human-interpretable feature) to determine positivity and eligibility for the targeted therapy. Assume this is the only test available for this therapy.
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
DP/CP | Digital pathology and computational pathology |
OOP | Out of pocket |
CDx | Companion diagnostic |
TROP2 | Trophoblast cell surface antigen 2 |
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Cohort | Segment | Percentage of Respondents |
---|---|---|
All Clinicians | All | 100% |
Geography | Northeast | 21% |
Midwest | 22% | |
South | 36% | |
West | 22% | |
Practice Type | Academic Hospital | 35% |
Community Hospital with Academic Affiliation | 10% | |
Community Hospital | 20% | |
Private Practice, Hospital, or Network-Affiliated | 14% | |
Independent Private Practice | 22% | |
Years in Practice | 0–1 | 6% |
2–10 | 36% | |
11–20 | 30% | |
21–40 | 29% | |
Anatomic Pathology Lab Most Commonly Used | In-House Lab | 29% |
Affiliated Hospital Lab | 5% | |
Commercial Reference Lab | 22% | |
Specialty Reference Lab 1 | 45% |
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Aggarwal, C.; Desai, A.; McConnell, N.; Cadirov, N.; Gustavsen, G.; Agarwal, A.; Chehab, N.; Kotapati, S.; Patel, N. Clinician Perspectives on Digital and Computational Pathology: Clinical Benefits, Concerns, and Willingness to Adopt. Diagnostics 2025, 15, 2527. https://doi.org/10.3390/diagnostics15192527
Aggarwal C, Desai A, McConnell N, Cadirov N, Gustavsen G, Agarwal A, Chehab N, Kotapati S, Patel N. Clinician Perspectives on Digital and Computational Pathology: Clinical Benefits, Concerns, and Willingness to Adopt. Diagnostics. 2025; 15(19):2527. https://doi.org/10.3390/diagnostics15192527
Chicago/Turabian StyleAggarwal, Charu, Aakash Desai, Nicholas McConnell, Nicholas Cadirov, Gary Gustavsen, Arushi Agarwal, Nabil Chehab, Srividya Kotapati, and Nikunj Patel. 2025. "Clinician Perspectives on Digital and Computational Pathology: Clinical Benefits, Concerns, and Willingness to Adopt" Diagnostics 15, no. 19: 2527. https://doi.org/10.3390/diagnostics15192527
APA StyleAggarwal, C., Desai, A., McConnell, N., Cadirov, N., Gustavsen, G., Agarwal, A., Chehab, N., Kotapati, S., & Patel, N. (2025). Clinician Perspectives on Digital and Computational Pathology: Clinical Benefits, Concerns, and Willingness to Adopt. Diagnostics, 15(19), 2527. https://doi.org/10.3390/diagnostics15192527