The convergence of laboratory medicine, novel whole-slide imaging technologies, and the availability of cloud storage has created a unique opportunity for patient care. At Mayo Clinic, state-of-the-art scanning facilities support the extensive digitization of pathology slides. The clinic’s robust infrastructure integrates AI for real-time quality control, ensuring high accuracy in digitizing archived tissue slides. By empowering pathologists without coding experience to develop their own algorithms with user-friendly tools, Mayo Clinic fosters inclusivity in innovation. The future of digital pathology at Mayo Clinic is multimodal. AI-driven mutation prediction algorithms and immersive VR environments designed for pathologists, by pathologists, may significantly enhance diagnostic precision, efficiency, and collaboration within the medical community.

The integration of artificial intelligence in clinical environments transcends simple data analysis, encompassing a complex interplay of multidimensional data, technological frameworks, procedural nuances, and interdisciplinary collaboration. The Clinical AI Readiness Evaluator (CARE) framework provides a rigorous methodology for navigating and optimizing this intricate landscape, enhancing the efficacy, responsibility, and sustainability of healthcare innovations through systematic evaluation of technological readiness levels (TRL).

The gap between pathologists and computer scientists in digital pathology often results in a misalignment of clinical needs and computational solutions. While pathologists may hesitate to adopt computer-aided diagnostic (CAD) tools due to limited understanding of their design and purpose, computer scientists may develop technically proficient algorithms that lack clinical relevance. A standardized framework for aligning clinical goals with computational execution can bridge this divide, fostering a shared language between disciplines. This approach aims to facilitate more synergistic collaborations that advance healthcare through clinically optimized digital pathology solutions.

Biobanks operate at the intersection of personalized medicine, big data, and artificial intelligence in modern healthcare. Organizations like the Cooperative Human Tissue Network (CHTN) are uniquely positioned to advance medical research by providing high-quality, well-characterized biospecimens essential for training and validating AI models for clinical use. The management of these valuable resources presents complex challenges spanning data governance, system interoperability, and ethical considerations in human tissue collection and utilization.

The implementation of artificial intelligence (AI) in pathology practice offers transformative potential for enhancing diagnostic efficiency and accuracy. Through careful examination of unmet clinical needs, we can identify opportunities where AI technology provides meaningful solutions. The convergence of digital pathology with deep learning technologies enables both generative and non-generative AI applications, creating new possibilities for advancing diagnostic capabilities while addressing existing challenges in pathology practice.

For two centuries, pathology practice has woven precise quantitative observations with rich qualitative analysis. Pathologist language is spun from this web, encoded in a unique parlance that efficiently communicates both measurable features and their clinical implications. While current machine learning approaches excel at specific quantitative tasks, their attempts to fully capture this expertise reveal a deeper challenge. Whether focusing on human-interpretable measurements or abstract deep-learning features, these approaches often miss the sophisticated interplay between observation and interpretation that characterizes pathology practice. Through tracing the evolution of histologic feature analysis, a path toward computational methods may emerge: one that truly complements pathology workflow - preserving the nuanced language pathologists use while enhancing their ability to derive insights from both qualitative and quantitative observations.