Generative AI in Anatomical Pathology: Today’s Innovations and Tomorrow’s Possibilities

Background:Generative Artificial Intelligence (AI) models such as Large Language Models, Vision Language Models, and Foundation Models have emerged as transformational tools across various disciplines. Anatomic pathologists are already interacting with generative AI chatbots during case sign out to get assistance on differential diagnosis work-up, recommended special stains as well as immunohistochemistry and molecular testing based on current guidelines, and to produce a draft of the pathology report. Generative AI is starting to reshape the technical and administrative processes within anatomic pathology too and holds immense potential for a host of new potential applications to completely transform the discipline of anatomic pathology and cancer diagnosis. Aims:This presentation aims to delve into the generative AI applications, advantages, and challenges in anatomic pathology, emphasizing its influence on technical laboratory processes and pathologist sign out procedures, to highlight the opportunities to make workflow efficiency gains. This talk will also note the impact of generative AI applications on the educational paradigms and research advancements specifically within anatomic pathology. Design:This presentation is based on a collaborative work conducted by a group of researchers and professionals encompassing pathology and AI fields who have conducted a thorough literature review into the recent developments in generative AI applications within anatomic pathology. These generative AI applications will be categorized into unimodal and multimodal applications and will be assessed for their current clinical utility, ethical implications, and future potential. Results:Generative AI exhibits substantial promise across several domains in anatomic pathology. AI-driven image analysis, virtual staining, and synthetic data generation significantly enhance diagnostic precision. Automation of routine tasks, quality control, and reflex testing demonstrates potential for considerable workflow improvements leading to quicker turnaround times assisting faster treatment and better patient outcomes. AI-generated educational materials, synthetic histology images, and advanced data analysis methods foster enhanced educational and research opportunities. Initial findings suggest anatomic pathology workforce seems cautiously optimistic about the transformative potential of AI. Pathologists show interest in adopting AI tools for non-diagnostic tasks. There is a growing spectrum of various applications in academic settings. Dependable AI tools will need to go through rigorous testing and evaluation before and after each implementation to ensure quality. Conclusions:Generative AI holds the potential to revolutionize anatomic pathology by enhancing diagnostic accuracy, improving workflow efficiency, and advancing education and research. However, its successful integration into clinical practice demands ongoing interdisciplinary collaboration, meticulous validation, and strict adherence to ethical standards to ensure that AI's benefits are fully realized while maintaining the highest levels of patient care. This talk will explore the transformative potential of generative AI in anatomic pathology, offering participants valuable insights into its current and future applications and addressing the necessary steps for its successful and ethical implementation in clinical practice. Keywords: Generative AI, Anatomic Pathology, Diagnostic Accuracy, Workflow Efficiency, Education, Research, Ethical Considerations

Learning Objectives

1. Understand generative AI and how various current applications of this technology are reshaping anatomical pathology.  improve diagnostic accuracy, streamline workflows, and boost research in anatomic pathology, with a focus on AI-driven image analysis and virtual staining.        
2. Discover how generative AI could be applied in near future to improve diagnostic accuracy, streamline workflows, boost research in anatomical pathology.
3. Identify the ethical and practical challenges of using generative AI in anatomical pathology and explore methods to ensure reliable and unbiased AI applications.