245: Why Going Slow Is Killing Digital Pathology Adoption | Syed T. Hoda, M.D.

Send us Fan MailIs your digital pathology rollout moving so slowly that it’s creating a fragmented workflow instead of transforming the department?In this episode of the Digital Pathology Podcast, I speak with Dr. Syed Hoda, Director of Digital Pathology at NYU, about why gradual implementation may no longer be the best approach to digital pathology adoption.Dr. Hoda explains how NYU used an intensive nine-month planning period to prepare for a department-wide transition. The process involved pathology, IT, project managers, vendors, hospital leadership, and approximately 40–50 people participating in regular planning calls.This wasn’t simply a scanner installation.The team mapped workflows, configured Epic Beaker, redesigned laboratory spaces, tested integrations, planned training, and addressed the practical concerns of nearly 100 pathologists.We also discuss why scanner specifications may matter less than integration, vendor support, training, and system performance. For Dr. Hoda, digital pathology had to work as smoothly as glass microscopy. Speed was non-negotiable.Change management played an equally important role. Through open discussions, town halls, and the ADKAR framework, the team addressed concerns ranging from ergonomics to the loss of collaborative microscope sessions.The result? Every pathologist adopted the digital workflow, no one left the department because of the transition, and approximately 60–65 pathologists now work remotely using equipment that matches their office setup.Finally, we examine the next step: artificial intelligence in pathology. Dr. Hoda explains why NYU focused on building a reliable digital foundation before introducing AI. He also raises important questions about validation, transparency, responsibility, regulatory clearance, and the need for greater pathologist involvement in AI development.Episode Highlights00:00 — Are we repeating the same mistakes with pathology AI?Dr. Hoda compares the current excitement around AI with the early promises made about digital pathology 15 years ago.01:04 — Meet Dr. Syed HodaHis clinical pathology background and path to becoming NYU’s Director of Digital Pathology.03:16 — Why going slowly can hold departments backHow partial adoption creates fragmented workflows, inconsistent training, and prolonged implementation.06:25 — Leadership support for rapid adoptionWhy institutional commitment, resources, and an ambitious timeline made the project possible.10:13 — Nine months of detailed planningWorkflow mapping, laboratory changes, system configuration, vendor selection, testing, and validation.11:48 — The role of professional project managementWhy pathologists shouldn’t be expected to coordinate every part of a complex digital transformation.14:29 — Why the scanner isn’t the most important decisionImage quality matters, but integration, service, training, and workflow fit may matter more.17:42 — People matter more than machinesHow vendor relationships and departmental engagement supported adoption.19:19 — Setting clear expectations across the departmentNYU communicated that every pathologist would move to digital sign-out within a defined period.20:49 — Change management is a structured processHow the ADKAR framework guided communication, education, adoption, and reinforcement.25:07 — Addressing practical and personal concernsFrom mouse ergonomics to preserving collaborative case review between pathologists.27:19 — Why NYU didn’t introduce AI firstDr. Hoda explains why pathologists needed to become comfortable with the digital platform before adding new AI tools.29:26 — Digital pathology and remote sign-outApproximately 60–65 pathologists now work remotely with equipment matching their office setup.30:28 — Why speed is non-negotiableEven a small delay or repeated pixelation can quickly undermine confidence in a digital workflow.33:25 — A cautious approach to pathology AIConcerns about premature adoption, self-validation, limited regulatory clearance, and lack of pathologist involvement.37:27 — Scientific validation, transparency, and responsibilityWhat happens when the AI result and the pathologist’s interpretation don’t agree?40:41 — Where AI could meaningfully augment pathologyQuantifying microenvironments, feature combinations, ratios, and findings that are difficult to assess visually.Resources MentionedADKAR change management frameworkDigital Pathology AssociationExecutive War CollegeFDA list of AI-powered medical devicesA radiology mock-trial paper examining responsibility when clinicians use AI: Examining perceptions of liability about AI in radiology (MedRxiv)Why AI cannot do good science without humans (Nature Editorial)A previous Digital Pathology Podcast discussion about AI-supported colorectal cancer feature analysis (How to use deep learning image analysis for colon cancer with Rish Pai)Listen to the full conversation for a practical look at digital pathology planning, change management, remote sign-out, scanner integration, and responsible AI adoption.Support the showGet the "Digital Pathology 101" FREE E-book and join us!