Why Greater AI Adoption in Clinical Labs Will Occur in 2025

Advancements in artificial intelligence (AI) have enabled its seamless integration into lab workflows no matter the platform being used, driving greater efficiency in diagnostic precision. These breakthroughs lay the foundation for further AI adoption in clinical labs in 2025.

Last year, pathology centered AI foundation models grew notably larger and smarter to provide greater capability and accuracy, particularly in their ability to detect common and rare cancers. By enhancing precision, AI empowers pathologists to deliver better outcomes for patients and others in cancer care who need these insights. With these advancements, pathology labs are positioned for continued innovation and a stronger role in transforming oncology and the broader healthcare ecosystem.

What To Expect with AI Adoption in Clinical Labs

When it comes to AI, there are three important areas clinical lab managers should watch closely in 2025:

• Expansion of AI adoption in clinical labs.
• Collaborative partnerships between AI and pathology platforms.
• Further focus on responsible AI.

Let’s briefly look at these three areas in more detail:

Expansion of AI adoption in clinical labs

Over the past five years, pathology labs have seen a rise in the number of slides they must evaluate for cancer diagnoses. Several factors drove this change, including the increase in cancer incidence, expanded screening programs, and advances in precision medicine, such as biomarker testing and personalized treatment plans, which demand more detailed and accurate analyses. 

Further, improved diagnostic standards, the need for follow-up biopsies during treatment monitoring, and pathology workforce shortages further amplify workloads, highlighting the critical need for efficiency without compromising accuracy. 

Additionally, the adoption of digital pathology introduces insight and workflows that can support both higher volumes and improved diagnostic precision, emphasizing the importance of integrating AI technology and optimized processes to meet the growing demands in cancer diagnostics. 

There have been great advances in therapies, but treatment cannot occur without timely and accurate diagnoses. For patients, speed, efficiency, and accuracy are critical. AI can enable all three, making it more pressing than ever to incorporate the technology into labs and digitize them to do so. We expect to see more movement in digital pathology adoption in 2025 as a result. 

Collaborative partnerships between AI and pathology platforms

In 2025, interoperability will become commonplace between AI and the industry’s leading pathology management platforms to ensure accessibility for each pathologist, regardless of the platform they use. This development will help remove barriers to adoption of digital pathology. Generally, digital transformation in labs requires changes to IT, workflows, and the labs themselves, all of which are daunting. However, greater interoperability means clinical labs do not have to switch platforms; AI can simply be used with their existing infrastructure to enhance their workflows. 

This collaborative approach fosters an interesting mix of cooperation and competition among technology providers, encouraging innovation and prioritization of patient outcomes. By enabling seamless integration, AI adoption in clinical labs will accelerate, benefiting the entire healthcare ecosystem through improved diagnostics and improved workloads for pathologists.

Further focus on responsible AI

In 2025, the responsible and ethical use of AI will take center stage, with potential guidelines being developed to emphasize transparency, fairness, and bias mitigation. 

Reputable technology companies recognize that ethical AI can help build trust, foster adoption, maintain safety, and drive responsible practices. By prioritizing these principles, innovation will uphold the highest ethical standards.

Downstream Applications and Open-Sourced AI to Drive Progress

The insights derived from pathology, which were previously confined to glass slides in labs, are now accessible thanks to these massive pathology AI foundation models.

In 2025, these AI insights will push the boundaries of what's possible, encouraging more breakthroughs in drug discovery, identifying important biomarkers, optimizing clinical trials with more precise patient selection, and accelerating the development of novel therapies that were previously out of reach.

Additionally, open sourcing will drive progress. We expect to see further innovation based on the models being open sourced, as this gives researchers and developers the ability to test their own technology and ultimately expand on what is capable in the landscape of cancer diagnosis and treatment. 

Open-source approaches will change the field and disperse technological power so that it is not vested in one single organization. As a result, researchers and developers will enjoy more access to the best technology, which in turn will increase AI adoption in clinical labs and other medical settings.