Decision support algorithms that deliver meaningful use in the anatomic pathology lab

Artificial intelligence (AI) in anatomic pathology (AP) has moved from exploratory pilots to targeted deployments that meaningfully support diagnosis, quality, and efficiency. The labs that see real impact are not deploying AI for its own sake—they are inserting decision support at precise points in the workflow where it improves clinical outcomes, reduces variability, and saves time without adding clicks or additional steps. This article is a practical masterclass on how to design, evaluate, and operationalize AI‑driven decision support in AP so it actually delivers meaningful use.

Identifying meaningful use for your lab

Meaningful use isn’t just model accuracy; it’s clinically relevant gains realized inside the everyday workflow. In AP, that typically means the following:

• Earlier review of cases that have been identified as having a high probability of being positive through triage and worklist prioritization
• Greater diagnostic consistency with standardized feature detection and quantitative scoring
• Fewer misses and near‑misses via region-of-interest (ROI) highlighting and secondary checks
• Faster reporting through structured extraction and synoptic assistance
• Higher throughput without burnout—minutes saved per case, fewer re‑cuts, less manual interaction

To be meaningful, the AI must produce actionable outputs — stat or priority identification; ROIs; probability estimates; and calculations that pathologists trust and can easily accept, adjust, or override.

Where decision support algorithms add real value

Case triage and worklist prioritization

AI models can assign a probability of malignancy or clinically significant findings and push those cases to the top of the pathologist’s worklist. In high‑volume subspecialties, such as prostate, breast core biopsies, colorectal resections, triage helps ensure that urgent cases are read earlier, often shaving hours off time‑to‑initial‑review. The key to this use case is lab-defined thresholds and the ability for the pathologist to override or re-prioritize when confidence is low.

Region‑of‑interest identification and heatmaps

Whole‑slide image (WSI) algorithms can identify likely tumors, mitotic cells, necrosis, perineural invasion, or lymphovascular invasion. These findings help pathologists focus attention where it matters and reduce the risk of oversight, especially in tedious, large‑section specimens. ROI overlays should be togglable and be able to be stored with the case.

Quantitative biomarker support

For immunohistochemistry and in situ hybridization, AI can count positive cells, compute H‑scores, quantify percentage positivity, and assist with HER2, ER/PR, Ki‑67, PD‑L1, and others. Even when the final call remains with the pathologist, quantitative consistency reduces inter‑observer variability and turnaround times while increasing efficiency and accuracy.

Quality control and pre‑analytic checks

Slide quality factors such as focus, tissue folds, artifacts stain intensity, and scanner anomalies can be flagged early to avoid wasted time on case routing to pathologists only to have to be sent back for re-cuts or re-scans, delaying the diagnosis. Pre‑analytic decision support is invisible when it works—fewer interruptions and smoother downstream throughput.

Synoptic and narrative assistance

Natural language processing (NLP) models can extract key facts from dictated text or draft synoptic templates for ease of the pathologist’s confirmation. The win isn’t creative writing — it’s completeness — no missing fields, consistency in terminology, and speed to final report. Guardrails, structured prompts, and image management system (IMS) or laboratory information system (LIS) integration are essential to prevent irregularities and to support the full benefit of automation.

The compelling factor of success

AP data is messy because elements vary by scanner, stain, protocol, and site. Strong foundations for algorithms include the following:

• Representative training sets that span multiple stains, scanners, magnifications, tissue types, and disease prevalence. AP models trained on one scanner and stain protocol often degrade and require additional training and validation when applied across additional ones.
• Region‑level annotations are required for tasks like mitosis detection and tumor identification and segmentation.
• Metadata discipline: Capture scanner model/firmware, stain protocol, batch/lot, magnification, tissue type, and timestamps. This is essential for drift detection and recalibration and for root‑cause analysis in anomaly detection.
• Standardization: Make sure that algorithms have normalized stains, are using consistent terminology, and define tile extraction logic consistently.
• De‑identification and security: Clear data governance, audit trails, and access controls ensure compliance and defensibility during algorithm development, tuning, and validation.

Treat your lab data like the valuable asset that it is—invest once in cleaned, labeled, well‑documented datasets and leverage that for additional revenue streams across multiple use cases.

Not just accuracy: Understand the value or success metric for adoption

Identify what your operational objectives are for deploying algorithms. Is it to improve patient care, increase operational efficiency, improve turnaround times, impact diagnostic accuracy or other? Pre‑determine the measurements of your success thresholds by defining what you consider clinically meaningful improvements, plus operational metrics (minutes saved per case, reduction in re‑cuts, fewer disparate reads). You will want to capture these data points  before implementation so that you can measure your improvement and ROI post-implementation. I recommend measuring at 3 months, 6 months, and 12 months post go-live to see your immediate and your longer term realized gains. It will also be eye opening to see how increased use and experience improves the ROI as time increases.

Another good metric to gather is measuring agreement with pathologist reads, turnaround time impact, and override patterns. This will help determine its actual value in practical, clinical applications.

Interoperability and true integration: The make-or-break factor for successful adoption

The fastest way to kill adoption is to force more manual steps in a pathologist’s workflow.  It also opens the opportunity for human error and mixing patient cases and AI output if you are working in multiple screens and applications.  Successful adoption, and results, happens when your AI algorithms are directly integrated within your digital platform or IMS. This is how the pathologist has access to all of the information that is related to that specific patient and case in a single, unified solution and workflow. It ensures that AI results appear automatically on the whole slide image. This also supports that AI outputs (ROIs, measurements, calculations) are stored with the case and are traceable in the audit trail.  This also supports the pathologist’s ability to accept, alter, or reject the algorithm’s results in their diagnosis. Again, tracking this information is key to determining the efficacy and value of the algorithm in your lab’s usage. If a feature or function adds clicks or dictates the decision, it’s not decision support—it’s decision obstruction and not very palatable to most pathologists and labs.

There are also several applications for AI in terms of the point of inflection for the algorithm. These should all be considered when determining the overall value and goals for your adoption and usage.  First read is when there are preset rules for which cases the algorithm will automatically be applied to prior to routing to the pathologist.  This workflow allows for the results to be available to the pathologist when they open the case and view the slide.  There is also ROI application where the pathologist can determine if, and when, to apply an algorithm and to which areas on the image in real-time. And second read or QC is when the algorithm is applied post diagnosis as an automated QC process and follows the percentage that you determine needs to go through a second read in your organization.  Each application is valuable and has its own benefits, and costs.

Be sure to pair hard metrics with user sentiment. Does the AI make pathologists more confident? Increase turnaround times? Improve accuracy? Reduce corrected cases? Adoption depends on both and often they are not a 50/50 result.

Conclusion

AI‑driven decision support in AP labs is most powerful when it reduces tedious, time consuming and manual tasks. When it is tightly integrated in your digital platform and provides beneficial outputs for the pathologist. When you deploy algorithms in a planned, measured and meaningful way, the gains compound—better patient care, happier pathologists, and a lab that can scale its expertise with an increasingly resource-constrained workforce.