Digital Pathology and AI in Everyday Practice: Practical Case Examples

The value of digital pathology and AI becomes much clearer when we move from theory to the cases that pathologists encounter every day. The following examples illustrate how these technologies can support routine diagnostic workflows.

Case 1: Breast Cancer — Supporting HER2 Assessment

A patient undergoes surgery for breast carcinoma. Histopathology confirms invasive carcinoma, and HER2 immunohistochemistry is requested.

Traditionally, the pathologist reviews the stained slide manually and determines the staining pattern and intensity according to established criteria.

With a validated digital pathology and AI-assisted system, the scanned slide can potentially be analyzed for tumor regions and membrane staining. The software may provide quantitative measurements or highlight areas requiring attention.

Practical benefit: Instead of replacing the pathologist's interpretation, AI can provide an additional quantitative assessment that may support consistency, particularly in borderline or difficult cases.

The final interpretation remains a professional decision based on the appropriate guidelines, morphology, clinical information, and ancillary findings.

Case 2: Prostate Biopsy — Finding Small Foci of Carcinoma

A prostate biopsy contains multiple cores, with some showing benign glands and others containing very small suspicious areas.

Reviewing numerous cores can be time-consuming, particularly when malignant involvement is focal.

A digital slide can be screened using an appropriately validated AI tool that highlights regions suspicious for carcinoma.

The pathologist can then review the highlighted regions along with the entire slide.

Practical benefit: AI can function as a screening or attention-directing tool, potentially helping the pathologist identify small suspicious areas more efficiently.

The important point is that the pathologist still reviews the complete specimen and makes the diagnosis.

Case 3: Colon Biopsy — Quantifying Tumor-Related Findings

Consider a colorectal carcinoma resection where multiple histological parameters need to be assessed.

Some measurements and counts are relatively straightforward but can become labor-intensive when performed repeatedly across large specimens.

Digital pathology can allow regions to be measured and annotated directly on the image. AI may assist with selected quantitative tasks where an appropriate validated algorithm is available.

Practical benefit: The combination of digital measurement and AI-assisted quantification can reduce manual workload and provide reproducible numerical information.

This can be particularly useful when quantitative parameters contribute to reporting, research, or quality-improvement activities.

Case 4: Lymph Node Examination — Searching for Metastatic Disease

A cancer resection may contain numerous lymph nodes. A pathologist may need to examine multiple sections from multiple nodes for metastatic deposits.

Digital pathology can make systematic review easier, while appropriately validated AI algorithms may assist in identifying suspicious regions.

Practical benefit: The technology can help direct the pathologist's attention toward areas that may warrant closer examination, particularly in large datasets.

However, absence of an AI alert should never automatically be interpreted as absence of disease. The complete slide must remain part of the diagnostic review.

Case 5: A Difficult Case Requiring a Second Opinion

A pathologist encounters an unusual tumor with morphology that is difficult to classify.

In a traditional workflow, glass slides may need to be physically packaged and transported to another institution or expert.

With digital pathology, the relevant slides can potentially be shared electronically with a consultant, subject to appropriate privacy, regulatory, and institutional requirements.

The consultant can review the same digital images and provide an opinion.

Practical benefit: Digital pathology can significantly reduce the logistical barriers associated with expert consultation and facilitate faster collaboration.

This can be particularly valuable for rare tumors and diagnostically challenging cases.

Case 6: Tumor Board Discussion

A patient with metastatic cancer is being discussed in a multidisciplinary tumor board.

The pathologist needs to demonstrate a particular histological feature to the oncology and surgical teams.

Instead of carrying glass slides or relying on a microscope connected to a camera, the pathologist can display the digital slide and navigate directly to the relevant area.

Annotations can be used to demonstrate specific morphological findings.

Practical benefit: Digital pathology turns pathology images into a shareable clinical communication tool, allowing other specialists to better understand the pathological findings.

Case 7: Teaching a Resident

A pathology resident is learning to recognize different patterns of chronic liver disease.

The consultant can open a digital slide, point out specific morphological features, annotate them, and compare them with other cases.

The resident can later access the same case for revision.

AI-based tools may eventually provide additional opportunities for interactive image analysis and case-based learning.

Practical benefit: Digital pathology allows teaching to become more visual, interactive, reproducible, and accessible.

Case 8: Reviewing a Previous Case

A patient returns several months later with a new biopsy.

The pathologist wants to compare the current specimen with the patient's previous material.

With a well-organized digital pathology system, previous digital slides can potentially be retrieved quickly and viewed alongside the new specimen.

Practical benefit: Direct visual comparison can help the pathologist assess whether the current findings are similar to, different from, or potentially related to the previous pathology.

This can be particularly helpful in oncology, where comparison with previous specimens is frequently important.

Case 9: Quality Assurance in Immunohistochemistry

A laboratory performs a large number of immunohistochemical stains every day.

Digital pathology can help laboratories create systematic image-based records and support review of staining quality.

AI may also assist with selected image-analysis tasks, such as assessing staining patterns or quantifying positive cells, when appropriately validated.

Practical benefit: Digital image analysis can complement existing laboratory quality-assurance processes and help identify variation that may require investigation.

Case 10: The Busy Morning Sign-Out

Imagine a typical morning: several biopsies, multiple surgical specimens, immunohistochemistry results, consultation cases, and a multidisciplinary meeting are all waiting for review.

The advantage of digital pathology is not necessarily that it makes every diagnosis automatic.

Its value is that it can bring multiple parts of the workflow into one connected environment.

The pathologist can access images, review previous cases, examine annotations, use validated analytical tools where appropriate, consult colleagues, and document findings within an integrated workflow.

This is where the real value of digital pathology and AI emerges—not from one dramatic futuristic application, but from dozens of small improvements across the working day.

The Most Important Point: AI Should Assist, Not Distract

These examples also highlight an important principle.

AI should not be introduced simply because it is technologically impressive. It should solve a genuine clinical or workflow problem.

A useful AI application should answer questions such as:

Does it improve efficiency? Does it provide reproducible quantitative information? Does it help identify relevant areas? Does it reduce repetitive work? Does it improve communication or education? Has it been appropriately validated for the intended use? Can the pathologist understand its limitations?

The goal should not be “AI everywhere.”

The goal should be “the right technology, for the right task, at the right point in the workflow.”

From Individual Cases to a New Pathology Workflow

These examples demonstrate that the impact of digital pathology and AI is unlikely to come from a single revolutionary application.

Instead, the transformation may occur gradually.

A pathologist may use digital slides for routine reporting, AI for a specific quantitative task, digital consultation for a difficult case, image sharing during a tumor board, and a digital teaching library for resident education—all within the same working environment.

Over time, these individual capabilities can become part of a connected pathology ecosystem.

That is why digital pathology and AI matter for day-to-day practice. They are not merely changing how we look at slides; they are changing how pathology can be performed, communicated, taught, measured, and integrated with the rest of medicine.

Conclusion

The future of pathology is unlikely to be about choosing between humans and machines.

It is about combining the strengths of both.

The pathologist brings experience, clinical reasoning, contextual understanding, and professional responsibility. Digital pathology provides accessibility, connectivity, and a platform for managing tissue images as data. AI can add quantitative analysis, pattern recognition, automation, and decision support to selected parts of the workflow.

The most successful pathologists and laboratories will not necessarily be those that adopt the greatest number of technologies.

They will be those that understand where technology genuinely adds value—and where human expertise remains indispensable.

The microscope may remain an essential symbol of pathology, but the modern pathology workflow is becoming increasingly digital, connected, and intelligent.