Industry Insights: Scanning the Path Ahead

Devika Gholap shares how smarter scanners and changing habits could accelerate the transition to digital pathology

By
Helen Bristow
October 15, 2026
7 min

The Pathologist

Overview

In an interview, OptraSCAN Chief Product Officer Devika Gholap identifies cost and established work habits as barriers to wider digital pathology adoption. She describes potential scanner–AI integration and automated slide and image quality control as developments that could support analysis and triage, while emphasizing that image quality depends on the quality of the input slide.

Background

Digital pathology uses whole-slide imaging to make tissue slides available as digital images for review and analysis. The interview describes adoption as slower than anticipated, citing the cost of scanners and systems relative to microscopes and the adjustment required to move from microscope-based work to screen-based navigation. The supplied related context also identifies technical, regulatory, and operational challenges in the digital transformation of pathology. The College of American Pathologists lists its 2021 guideline on validating whole-slide imaging for diagnostic use as current guidance.

Data Highlights

The interview provides no numerical trial or performance data. It discusses adoption barriers and potential scanner, AI, and quality-control capabilities qualitatively.

Key Findings

Gholap identifies cost as a major adoption barrier, noting that digital pathology scanners and systems can cost many times more than a microscope that may cost a few thousand dollars.
She describes pathologists’ established microscope habits and the adjustment to screen-based navigation as another barrier; she says trainees are increasingly being trained with digital pathology.
She envisions scanners capturing images while algorithms analyze them, with preliminary analysis available when images reach the pathologist.
She describes automated triage, such as separating normal from abnormal or tumor from non-tumor cases, as a potential next step rather than an established capability.
She characterizes slide and image quality control as foundational, describing algorithms that can assess image quality and flag images that do not meet required standards, as well as identify issues related to tissue or sample preparation.
The CAP lists its 2021 evidence-based guideline for validating whole-slide imaging for diagnostic use as current guidance; the supplied context says it addresses validation study design and performance benchmarking before clinical deployment.

Clinical Implications

The interview highlights the need to account for scanner and system costs, workflow adjustment, and slide and image quality when laboratories consider digital pathology. For diagnostic use of whole-slide imaging, the CAP guideline listed in the supplied context provides validation guidance; the interview’s proposed AI analysis and triage functions are described as potential developments, not established clinical outcomes.

Conclusion

Gholap’s account presents cost, user adaptation, and input quality as central considerations in digital pathology adoption. Scanner-integrated analysis, triage, and automated quality control are described as directions for technology development.