The application of AI in pathology benefits from the users of these tools possessing a fundamental understanding of what it takes to develop and put an AI model into practice. This includes not only existing pathologists and technical staff but also future practitioners. Knowledge and experience in AI also encourages engagement and interest, which is vitally needed to stave off workforce shortages and promote recruitment into the specialty. Extending AI fluency to our patients is also necessary to build trust in these burgeoning technologies and is essential for their ability to make informed decisions about their own care. To address each of these needs, we developed an educational approach that seeks to expose practitioners and patients to AI through experiential learning. The framework includes: 1) implementing infrastructure to directly support AI studies, including democratizing AI through a no-code AI platform, 2) establishing an internship program to reach learners within and outside the institution, 3) leveraging formal engagements with internal programs and mentoring AI related projects, 4) formalizing relationships with external academic institutions focused on engineering and computer science, 5) reaching the local community by upskilling area high school teachers using a train-the-trainer approach.First we implemented a no-code AI solution designed to provide our pathologists, technical staff, and trainees the ability to pursue their own AI projects without programming expertise. We created an RFA process to support over 50 projects based primarily on whole-slide imaging. Several publications and conference abstracts were generated as a direct result of this effort. In parallel, we sought to complement institutional cloud computing infrastructure with on-premises computing resources targeted to junior faculty and trainees through the Major Research Instrumentation program at the NSF.Second, we established an internship program to provide learners outside the institution with opportunities to engage in ongoing AI research projects with translational potential, mentored by established AI investigators and with access to large data sets and pathology expertise. Internships varied from 2 to 6 months and interns were at the early undergraduate, graduate, and postgraduate levels, representing medical and computing trainees alike. Our initial cohort of 14 interns were involved in projects across multiple pathology divisions and generated several first-author publications.Third, we collaborated with formal programs within our institution to provide trainees with opportunities to fulfill their educational requirements through involvement in AI projects. These trainees, often fellows, actively seek projects to gain practical experience in clinical AI. Initially, we engaged with the Clinical Informatics fellowship program, where second-year fellows acquire first-hand knowledge by participating in operational and research activities across the enterprise. Throughout the year-long engagement, our first fellow made significant contributions to the formalization of our AI lifecycle processes.Fourth, existing formal agreements between the Mayo Clinic and other academic institutions were leveraged to involve trainees in AI and digital pathology research, complementing Mayo Clinic's healthcare focus with the engineering and computing academic expertise found elsewhere. We participated in new programs as well, including co-op programs, capstone projects, and collaborative research opportunities.Fifth, we established a collaboration with the Mayo Office of Education to pursue extramural funding to support the Research Experiences for Teachers program focused on AI in Healthcare. The intent of the program is to introduce AI/healthcare curricular modules to area high schools with a focus on those in rural districts less likely to have access to these resources. This program adopts a train-the-trainer approach by providing AI research experiences integrated with AI curriculum support to area high school teachers, who will then implement curricula in their schools incorporating lessons learned from their summer research experience. We received letters of collaboration from 11 partner school districts, primarily in rural districts throughout Olmstead County. More than 10 research faculty at Mayo Clinic pledged to participate as project mentors within and beyond pathology.Together, these initiatives focused on extending AI education to all levels of learner from high school to graduate level to professional, and ensuring that learners outside the institution got access to the unique opportunities available at an institution well along in its digital journey. We suggest that an experiential-focused approach, especially when complemented by lectures, online content, and traditional didactics, can serve as a blueprint for delivering AI education to existing and future workforce in pathology.

Learning Objectives

1. Weigh the benefits of experiential learning against more conventional methods of learning and understand the practical role of each.        
2. Match specific target learners to an educational strategy tailored specifically to their goals. 
3. Establish an experiential education program at their own institutions that suits the specific needs and goals of the practice.

Authors: Christopher Garcia, MD1, Mark Zarella, PhD1, Chhavi Chauhan, PhD2Affiliations:1. Mayo Clinic, Rochester, MN2. Israeli Association for Ethics in Artificial IntelligenceAbstract: As artificial intelligence (AI) advances and integrates into digital and computational pathology, it is crucial to address the potential risks associated with equity and bias in these systems. Bias in AI can arise from various sources, including unrepresentative training data, flawed algorithm designs, and human biases embedded in the development process. These biases can lead to disparities in diagnostic accuracy, treatment recommendations, and ultimately, patient care. To ensure that AI models perform effectively in real-world scenarios, it is essential to design studies that accurately reflect the intended clinical use case, the characteristics of the target patient population, and the data processing methods used in clinical practice. Failure to align study design with these real-world factors can result in models that do not generalize well to their intended applications, leading to suboptimal performance and potential risks to patient care.This roundtable discussion aims to raise awareness of equity and bias issues in AI for digital and computational pathology, demonstrate their relevance in current practice, and share rapidly evolving best practices for mitigating risks while promoting transparency. We will explore real-world examples of how equity and bias manifest in digital and computational pathology, examining the implications of these issues on patient care, research, and the overall trustworthiness of AI systems. By delving into case studies and current research, we aim to provide attendees with a comprehensive understanding of the challenges at hand.Furthermore, we will discuss the rapidly evolving best practices for developing and deploying AI solutions responsibly, including strategies for ensuring diverse and representative training data, implementing robust validation processes, and promoting transparency in AI development. We will also highlight the importance of multidisciplinary collaboration, involving pathologists, computer scientists, ethicists, and other stakeholders in the development and evaluation of AI tools.Attendees will gain valuable insights into the current landscape of equity and bias in AI for digital and computational pathology, learning practical approaches for mitigating risks, promoting transparency, and fostering trust in AI-assisted pathology workflows. By the end of the roundtable, attendees will be equipped with the knowledge and tools necessary to advocate for responsible AI practices in their own institutions and research endeavors.As the field of digital and computational pathology continues to evolve, it is imperative that we proactively address the challenges of equity and bias in AI. This roundtable discussion serves as a call to action, encouraging the pathology community to engage in ongoing dialogue, collaboration, and education to ensure the responsible development and use of AI solutions. Together, we can harness the potential of AI to transform pathology while upholding the highest standards of equity, fairness, and patient care.

Learning Objectives

1. Understand issues surrounding equity and bias in AI for digital and computational pathology.
2. Learn practical approaches for mitigating risks, promoting transparency, and fostering trust in AI-assisted pathology workflows.
3. Advocate for responsible AI practices in their own institutions and research endeavors.