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

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Is 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 Highlights

• 00:00 — 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 Hoda
  His clinical pathology background and path to becoming NYU’s Director of Digital Pathology.
• 03:16 — Why going slowly can hold departments back
  How partial adoption creates fragmented workflows, inconsistent training, and prolonged implementation.
• 06:25 — Leadership support for rapid adoption
  Why institutional commitment, resources, and an ambitious timeline made the project possible.
• 10:13 — Nine months of detailed planning
  Workflow mapping, laboratory changes, system configuration, vendor selection, testing, and validation.
• 11:48 — The role of professional project management
  Why pathologists shouldn’t be expected to coordinate every part of a complex digital transformation.
• 14:29 — Why the scanner isn’t the most important decision
  Image quality matters, but integration, service, training, and workflow fit may matter more.
• 17:42 — People matter more than machines
  How vendor relationships and departmental engagement supported adoption.
• 19:19 — Setting clear expectations across the department
  NYU communicated that every pathologist would move to digital sign-out within a defined period.
• 20:49 — Change management is a structured process
  How the ADKAR framework guided communication, education, adoption, and reinforcement.
• 25:07 — Addressing practical and personal concerns
  From mouse ergonomics to preserving collaborative case review between pathologists.
• 27:19 — Why NYU didn’t introduce AI first
  Dr. Hoda explains why pathologists needed to become comfortable with the digital platform before adding new AI tools.
• 29:26 — Digital pathology and remote sign-out
  Approximately 60–65 pathologists now work remotely with equipment matching their office setup.
• 30:28 — Why speed is non-negotiable
  Even a small delay or repeated pixelation can quickly undermine confidence in a digital workflow.
• 33:25 — A cautious approach to pathology AI
  Concerns about premature adoption, self-validation, limited regulatory clearance, and lack of pathologist involvement.
• 37:27 — Scientific validation, transparency, and responsibility
  What happens when the AI result and the pathologist’s interpretation don’t agree?
• 40:41 — Where AI could meaningfully augment pathology
  Quantifying microenvironments, feature combinations, ratios, and findings that are difficult to assess visually.

Resources Mentioned

• ADKAR change management framework
• Digital Pathology Association
• Executive War College
• FDA list of AI-powered medical devices
• A 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.

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