We asked – you answered. The results of our readers' survey on lab priorities, trends, and challenges for 2026 are in!
But what do the data reveal about the pace of change, levels of optimism, and key drivers in the lab right now? We invited industry experts Nathan Buchbinder, Chief Strategy Office, Proscia, and Bilal R. Ahmad, Chief Executive Officer, PIROette Dx, to analyze the results and offer their insights.
The overall trends
Nathan Buchbinder: Overall, the results suggest pathology is entering a more disciplined phase of modernization. The debate has shifted from whether to adopt new technology to how to do so responsibly in a mission-critical clinical environment.
Respondents consistently emphasize outcomes over novelty, seeking tools and approaches that help manage growing diagnostic complexity, support overstretched teams, and deliver value at scale. The strong focus on workforce readiness and integrated AI adoption – alongside lower urgency for automation or remote collaboration – points to a belief that sustainable progress depends on building the right foundations first. In that context, the field’s optimism reflects confidence not in any single breakthrough, but in a more mature, execution-focused path forward aligned with current operating models.
Bilal Ahmad: What I take from these results is that pathology is moving from “digitize slides” to “digitize decision-making.” The center of gravity is shifting toward integrated, end-to-end workflows – images, metadata, analytics, and reporting – because that’s where the real value compounds.
The strong vote for AI/data integration and advances in diagnostics reflects a desire to keep up with accelerating clinical complexity, but the parallel emphasis on workforce evolution is telling: people recognize that technology only pays off when it’s adopted safely, consistently, and at scale.
In other words, the field isn’t chasing novelty – it’s looking for a production-grade “pathology operating system” that reduces cognitive burden, standardizes quality, and expands capacity without compromising trust.
Adoption of advanced technologies continues at a pace of measured urgency
NB: Most laboratories are no longer debating whether to modernize, but deciding what to modernize and how fast to move. The results suggest they’re moving forward deliberately. That pace makes sense in mission-critical pathology work, where change has to be implemented carefully to protect quality and continuity while staying disciplined with limited budgets and resources.
BA: In my most recent practice, the pace is “measured urgency.” Labs like ours feel the pressure – workforce constraints, rising case complexity, and increasing expectations for speed and standardization. But we also know pathology is mission-critical, so adoption has to be staged: validate, integrate, train, then scale.
Generally speaking, the fastest adopters aren’t necessarily the ones buying the most tools; they’re the ones building repeatable workflows and governance, so new technology doesn’t become another layer of friction. I see a clear trend toward pilots that prove clinical and operational value, followed by deliberate enterprise rollout.
Pathology practice will be shaped by increasing diagnostic complexity
NB: The strongest signal here is that pathologists see the next wave of impact coming from the intersection of AI and increasingly complex diagnostics. Respondents are emphasizing practical benefits – capabilities that help teams interpret more data, standardize decisions, and keep pace as testing becomes more advanced and information-rich.
It’s also notable that “digital and computational pathology” aren’t emphasized as much on their own. That may reflect that digitization is increasingly viewed as a prerequisite, while the near-term differentiation is the value delivered once workflows are digital and AI is applied: improving efficiency, consistency, and clinical confidence.
BA: Integration of AI and data-driven tools is the headline for a reason: the problem in modern pathology isn’t generating information – it’s synthesizing it reliably at the point of diagnosis. The “advances in diagnostics” vote fits that same theme: more biomarkers, more modalities, more data, more decisions. Digitization itself is increasingly viewed as table stakes; what differentiates labs in the near term is what they can do once digital – decision support, standardization, quality assurance, and faster translation of complex results into clinically actionable reports.
Investment priorities reflect sustainability concerns
NB: For 2026, the investment priorities suggest labs are thinking less about “buying the newest tech” and more about building sustainable capability. The top emphasis on workforce evolution and training indicates leaders see people, change management, and adoption readiness as the primary bottlenecks, not just access to tools. This is further supported by the strong showing for integrating AI and data-driven tools, which signals that labs are prioritizing the practical adoption needed to use AI consistently at scale.
The relatively lower prioritization of automation/efficiency and remote working and collaboration doesn’t imply those areas are unimportant. It more likely reflects that many labs view them as downstream benefits of broader digitization and workflow integration, rather than standalone initiatives to prioritize first.
BA: Workforce evolution and training appears to be the respondents highest priority for investment. This is the bottleneck because adoption is a people-and-process problem as much as a technology problem – especially when you’re trying to shift primary diagnosis into a digital workflow responsibly.
The strong emphasis on AI/data integration also makes sense: siloed tools create “invisible labor,” while integrated tools reduce handoffs and rework. Operational efficiency can produce outsized return on investment, but in pathology efficiency tends to be a result of foundational investments – workflow redesign, interoperability (LIS/IMS), standardization, and training – rather than a standalone automation project.
The promise of efficiency gains brings a sense of optimism
NB: What’s most striking in these responses is a sense of grounded optimism. Pathologists are excited less by any single breakthrough and more by the cumulative progress taking place across the field – particularly the growing maturity of digital and AI-enabled approaches. Respondents point to practical benefits like better decision support, improved consistency, and more sustainable ways of working.
It’s also telling that remote working and enhanced collaboration via digital pathology show up strongly as areas of excitement, even if they rank lower as near-term investment priorities. That contrast suggests once again that many labs see remote collaboration as a downstream payoff of modernization.
BA: I’m excited by the convergence of digitization, integrated data, and assistive AI. This combination is finally becoming practical enough to change daily work – not just in research settings.
The most promising future isn’t AI replacing expertise; it’s AI making expertise more scalable. It affords opportunities for surfacing the right regions, standardizing quantification, flagging discordance, and helping us manage complexity with more consistency. If we do it right, the payoff is better quality, faster turnaround where it matters, and a more sustainable profession – especially as workforce constraints persist.
Expertise must evolve in line with the need for integrated diagnostics
NB: The results point to a convergence of challenges rather than a single obstacle. The increasing complexity of the molecular diagnostics landscape rises to the top, reflecting how quickly pathology is becoming more data-rich and multidisciplinary. In this environment, synthesizing information is often more challenging than generating it.
Workforce constraints remain closely linked, as rising complexity increases the cognitive and operational burden on already stretched teams. The prominence of education reform in the results suggests many respondents see it as a critical lever to address both of these issues – by better preparing the next generation of pathologists to navigate integrated data, new technologies, and evolving diagnostic workflows, as well as attracting more students into the field.
BA: The biggest challenge is keeping diagnostic quality and trust intact while complexity accelerates. Pathologists are being asked to integrate morphology, IHC, molecular, and clinical context at increasing speed – often with fewer people. That makes implementation risk (workflow disruption, fragmented systems, inconsistent adoption) as important as the technology itself.
Education reform and workforce development are critical because we need both domain expertise and digital fluency. The labs that win will be those that operationalize change: employing validated workflows, clear governance, and integrated platforms that reduce cognitive load, rather than adding one more disconnected tool or dashboard.
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