Winning Strategies in the Next Decade in Pathology: Key Takeaways from Executive War College

AI is already transforming pathology’s day-to-day efficiency. “Even in our practice, we really have seen multiple and numerous workflow-related efficiencies. That’s already established,” noted Dr. Mohamed Salama. “Diagnostic accuracy and standardization? That’s coming next.” 

In essence, AI’s most immediate and proven value in pathology is operational efficiency: reducing turnaround time, manual slide handling, and costs, while setting the foundation for future gains in diagnostic accuracy, precision medicine, and integrated care.

Dr. Anil Parwani has implemented AI algorithms for prostate, breast, and other cancers in his practice. He described three key ways AI is transforming pathology: workflow optimization before case review, augmented analysis during slide interpretation, and built-in quality assurance after review. He described how AI can even prioritize cases, “putting the cases which have cancer ahead in the pile,” as he put it; still a “pile,” even if it’s now entirely digital.

AI is not just making pathology faster; it’s making expertise more accessible and effective, allowing a limited workforce to keep up with growing demand while improving diagnostic quality. Reflecting on how this changes the role of the pathologist, Dr. Derek Welch noted, “Even though the tools are not absolutely perfect yet, a pathologist wielding those AI tools is better than either one of them alone.”

The real ROI of digital pathology and AI is not just speed. It’s the systematic redistribution of routine diagnostic work to automation, freeing clinicians to focus on higher-value decision-making while improving consistency and scalability across the lab. 

“If you have 100 cases to review today, how can some of those tasks be more automated?” Dr. Parwani asked. “There are tasks which are autonomous…that can take away a lot of the tasks that a pathologist would spend doing, measuring things, annotating things.”

Digital pathology is also enabling consolidation of labs. This is a “benefit when it comes to the go forward for pathology and hospital systems,” noted Dr. Welch. 

In a hospital system with multiple hospitals, there is often a lack of homogeneity across pathology providers. Dr. Welch referred to it as “a quilt work of different, independent pathology groups.” This fragmentation is exactly what makes consolidation so critical moving forward. Digital pathology enables consolidation by decoupling pathologists from physical locations, allowing cases, expertise, and workflows to be centralized and managed across an entire health system rather than within individual hospitals. And, just as importantly, it allows health systems to tap into subspecialty expertise on demand, without duplicating resources across sites. Increasingly, this is not just an advantage, but a necessity for health systems operating in the private sector.

According to Dr. Salama, ROI for digital pathology and AI must be evaluated across multiple dimensions, most importantly, quality and clinical impact, not just productivity.

1. Workflow efficiency: The most directly measurable component of ROI, including reductions in manual steps such as slide retrieval and improvements in overall process efficiency.
2. Diagnostic quality and patient safety values: Historically seen as subjective, but increasingly measurable through metrics such as amended report rates, misdiagnoses, second opinion concordance, and overall error reduction.
3. Downstream clinical revenue impact: The ability to drive additional value through areas such as companion diagnostics, clinical trial enrollment, and more informed utilization or avoidance of molecular testing.
4. Workforce resilience and strategic positioning: Strengthening access to specialty and subspecialty expertise, improving recruitment and retention, and positioning pathology as a more integral, future-ready part of the health system.
    

The main takeaway from the panelists: AI is already enabling faster, more efficient subspecialty support today by automating routine tasks, triaging cases, and augmenting diagnostic insight, though its use must remain grounded in validated, patient-safe applications.

The main takeaway from the panelists: Organizations are shifting to data-driven management models, introducing new quality metrics, end-to-end workflow visibility, and dedicated digital teams to ensure adoption is measurable, accountable, and aligned to ROI and clinical value.

As this discussion made clear, the next decade in pathology will not be defined by a single breakthrough, but by the coordinated advancement of digital infrastructure, AI-enabled workflows, and predictive diagnostics working in concert. The leaders on this panel emphasized that success will depend on more than adopting new tools; it will require enterprise-wide alignment, measurable outcomes, and a clear vision for how pathology contributes to patient care across the continuum.

AI is already delivering tangible gains in efficiency and scalability, while laying the groundwork for improved diagnostic accuracy and more personalized treatment strategies. At the same time, digital pathology is enabling new operating models, from system-wide consolidation to expanded access to subspecialty expertise. Together, these innovations are redefining pathology’s value, not as a back-end service, but as a strategic, data-driven engine for clinical decision-making and precision medicine.

In summary, organizations that invest in operational readiness, embrace data-driven management, and evaluate ROI through the lens of quality, clinical impact, and long-term resilience will be best positioned to lead. The opportunity is clear: to elevate pathology into a more integrated, insight-driven discipline that delivers measurable value for patients, providers, and health systems alike.