Pathology has entered a bold new frontier: the PathoVerse. In this emerging universe, pathologists and institutions are using new technologies and methods that are redefining the delivery of precision medicine, with pathology at its core.

Yet every new universe presents challenges. The PathoVerse demands new skills, new approaches, and adaptability. Navigating it successfully requires more than technology – it requires a trusted guide.

In this session, with insight from PathPresenter partners across storage, compute, scanners, and AI, we’ll chart the course for thriving in the PathoVerse, from identifying and capturing ROI to unlocking AI’s full potential. 

Join us to discover how to make the journey through the PathoVerse a successful one.

Introduction:While advancements in artificial intelligence (AI) are rapidly impacting healthcare, its adoption in anatomic pathology is lagging. Although AI tools demonstrate potential for improving diagnostic accuracy, prognostic assessments, and workflow efficiency, many pathologists are hesitant to integrate them into routine practice. This reluctance is primarily due to concerns about model development transparency, limited clinical validation data, and a lack of practical, hands-on evaluation opportunities. Currently, most exposure to AI is passive, such as conference presentations or vendor demonstrations, preventing pathologists from directly assessing the tool's applicability to their specific clinical workflows.MethodsThe CAP AI Playground:To bridge this gap, the College of American Pathologists (CAP) has developed the AI Playground, a secure, interactive simulation platform replicating a pathologist's digital workflow environment. This simulated Laboratory Information System (LIS) interface enables pathologists to actively interact with AI models in a controlled, risk-free environment. CAP members can securely authenticate and access a range of AI algorithms alongside curated, annotated test datasets. These datasets include examples of true positives, false negatives, and edge cases that highlight model limitations. Each AI module is paired with comprehensive vendor-provided documentation detailing model training methodology, intended clinical use, and known biases or limitations. Pathologists can evaluate the AI's performance within a simulated sign-out viewer and provide structured feedback directly to developers, fostering an iterative model refinement process and enhancing trust through transparency.Results: Pathologists gain practical, hands-on experience with AI models in pathology-specific workflows, enhancing their understanding of model capabilities and limitations, and building confidence in AI-assisted diagnostic decision-making.AI Developers receive valuable user-driven feedback from pathologists, enabling rapid model iteration and improving clinical relevance. This early engagement accelerates the transition of AI tools into clinical practice.Conclusion:By providing a transparent, experiential platform for AI model evaluation, the CAP AI Playground addresses a critical barrier to the broader adoption of AI in clinical pathology. It empowers clinicians to critically assess, validate, and ultimately trust AI models, facilitating a more informed and efficient integration of these technologies into routine diagnostic workflows. This approach positions pathology at the forefront of healthcare's digital transformation. Future plans include expanding access to oncologists and other clinical specialists to foster greater interdisciplinary collaboration and integration of AI-driven insights.

Learning Objectives: 

1. Understand methods to test AI models
2. Understand AI technology and how it could be integrated safely into clinical practice
3. Demonstrate pathway to be involved in AI model development