Frameworks for AI-Enabled Pathology
As pathology departments integrate AI into diagnostic workflows, many face uncertainty around selecting vendors, validating models, and ensuring long-term oversight. This module offers a structured approach to evaluating third-party tools, assessing clinical and technical validity, and setting up sustainable governance structures that pathologists can trust.
Vendor Evaluation
With the proliferation of AI tools for histopathology, cytology, and whole-slide imaging, it is no longer enough to assess a vendor based on their pitch deck or peer-reviewed publication. Pathologists evaluating potential partners should ask:
What datasets were used to train and validate the model, and how representative are they of our patient population?
Has the model been tested on whole-slide images from multiple scanner vendors?
Can the vendor provide raw performance metrics, including sensitivity, specificity, false-positive rates, and case-level errors?
How transparent is the algorithm’s decision-making process, and can we audit its outputs?
A 2024 review published in NPJ Digital Medicine emphasized the importance of clinical validation across diverse populations and settings, noting that even high-performing models often fail under distribution shift (Ngiam et al., 2024).
Institutions should also consider the vendor’s approach to post-deployment support. This includes model retraining, error monitoring, and compatibility with existing PACS or LIS systems. A well-regarded algorithm that cannot be integrated cleanly into the daily diagnostic workflow can still become a source of inefficiency.
Algorithm Validation: Beyond the AUC
While many AI developers showcase strong performance through metrics like AUC-ROC, real-world pathology use demands deeper validation. External validation on your own datasets, or a close match thereof, is critical. Factors such as tissue preparation methods, staining variability, and scanner resolution can all influence model accuracy.
Many groups now use a dual-reader approach to assess performance. In this method, two human pathologists independently review cases alongside the algorithm, allowing for comparative analysis. Discrepancies can then be adjudicated to determine whether AI contributes to diagnostic confidence or confusion.
Moreover, validation must consider how the tool will be used. For example, algorithms that triage slides for review carry different safety implications than those offering primary reads. As noted in JAMA Network Open (Campanella et al., 2023), the clinical context dramatically shapes the threshold for acceptable model performance.
Governance: Creating Oversight Pathways That Work
No AI deployment in pathology is complete without ongoing governance. This includes processes for revalidation, performance drift detection, and documentation of error cases. Governance should not be seen as a hurdle but as a mechanism for maintaining clinical confidence.
One practical approach is to establish an internal AI Oversight Committee that includes both diagnostic pathologists and IT leads. This group can set criteria for onboarding new tools, define monitoring protocols, and periodically review outcomes.
Data versioning, audit trails, and detailed change logs should be maintained for every AI tool used. These elements are increasingly expected by regulators and are foundational for any pathology department planning to use AI in regulated environments.
As AI becomes more embedded in pathology workflows, the rigor of vendor evaluation, algorithm validation, and governance will determine whether the technology enhances or undermines diagnostic quality. These responsibilities increasingly fall to experienced pathologists, who are uniquely positioned to bridge clinical nuance with technical implementation. Approaching these steps with clear structure and accountability will lay the foundation for safe and effective AI adoption.
References
Ngiam, K. Y., et al. (2024). Clinical validation of AI across diverse populations and settings. NPJ Digital Medicine.
Campanella, G., et al. (2023). Clinical context shapes AI performance thresholds. JAMA Network Open.
