Democratizing Artificial Intelligence in Anatomic Pathology.

Flotte, Thomas J.; Derauf, Stephanie A.; Byrd, Rachel K.; Kroneman, Trynda N.; Bell, Debra A.; Stetzik, Lucas; Lee, Seung-Yi; Samiei, Alireza; Hart, Steven N.; Garcia, Joaquin J.; Beamer, Gillian; Westerling-Bui, Thomas

Context.--Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation. Objective.--To develop a supportive ecosystem that enables pathologists with variable expertise in artificial intelligence to create algorithms in a development environment with seamless transition to a production environment. Design.--Platform requirements included: (1) approachable and intuitive user interface, (2) diverse algorithmic modeling options, (3) support capability for internal and external collaborations, (4) a seamless mechanism for discovery to clinical deployment transition, (5) the ability to meet minimum institutional requirements for information technology (IT) review, and (6) the ability to be scalable over time. The ecosystem required platform education, data science guidance, project management structure, and ongoing leadership. Results.--The development team considered internal development and vended solutions. Because of the extended timeline and resource requirements for internal development, a decision was made to use a vended solution. Vendor proposals were solicited and reviewed by pathologists, IT, and security groups. A vendor was selected and pipelines for development and production were established. Proposals for development were solicited from the pathology department. Eighty-four investigators were selected for the initial cohort, receiving training and access to dedicated subject matter experts. A total of 30 of 31 projects progressed through the model development process of annotating, training, and validation. Based on these projects, 15 abstracts were submitted to national meetings. Conclusions.--Democratizing artificial intelligence by creating an ecosystem to support pathologists with varying levels of expertise can break down entry barriers, reduce overall cost of algorithm development, improve algorithm quality, and enhance the speed of adoption.

DATA science; PATHOLOGY; INTERPROFESSIONAL relations; SYSTEMS development; ARTIFICIAL intelligence; ANATOMY; ECOSYSTEMS; INFORMATION technology; DESCRIPTIVE statistics; LONGITUDINAL method; MACHINE learning; ALGORITHMS; USER interfaces

Archives of Pathology & Laboratory Medicine, 2025, Vol 149, Issue 1, p55

0003-9985

Academic Journal

10.5858/arpa.2023-0205-oa