Digital Pathology Podcast

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Where is AI in pathology actually becoming useful right now? In this episode of DigiPath Digest, I review 4 new PubMed papers across digital pathology, whole slide imaging (WSI), computational pathology, medical education, forensic pathology, and breast cancer AI. We look at a deep learning tool for coronary artery stenosis measurement in forensic autopsies, an AI-powered digital pathology model for renal pathology education, an open-source quality control tool for prostate biopsy whole slide images, and a breast cancer stage prediction model built for resource-constrained settings using low-magnification H&E slides. I also share updates on the upcoming second edition of Digital Pathology 101 and the decision to make AI paper summaries public on the podcast feed to help busy pathology professionals stay current. 
Highlights
 
[01:28] Update on the upcoming second edition of Digital Pathology 101 and the release of public AI paper summaries for faster literature review.

[05:22] Paper 1: Deep learning for coronary artery stenosis evaluation in forensic autopsies using whole slide imaging. Why objective stenosis measurement matters, how the model outperformed visual estimates, and why this could affect adoption in forensic pathology.

[15:18] Paper 2: AI-powered digital pathology with case-based teaching in renal education. A practical discussion on annotated digital slides, flipped classroom learning, and how digital pathology can improve pathology education and diagnostic reasoning.

[21:34] Paper 3: Open-source AI for quantitative quality control in prostate biopsy whole slide images. Why WSI quality control matters, what PathProfiler measures, and how automated QC can support remote pathology workflows.

[32:38] Paper 4: Breast cancer stage prediction from H&E whole slide images in resource-constrained settings. A look at low-magnification AI, vision transformers, and what moderate performance can still mean when access to advanced testing is limited.

[45:06] Closing thoughts, invitation to vote for future AI paper summaries, and a final reminder to download Digital Pathology 101. 
Resources
Paper 1: Development of a deep learning-based tool for coronary artery stenosis evaluation in forensic autopsies using whole slide imaging
PubMed: https://pubmed.ncbi.nlm.nih.gov/41998396/

Paper 2: Integrating AI-Powered Digital Pathology With Case-Based Teaching: A Novel Paradigm for Renal Education in Medical School
PubMed: https://pubmed.ncbi.nlm.nih.gov/41995002/

Paper 3: Application of an open-source AI tool for quantitative quality control in whole slide images of prostate needle core biopsies - a retrospective study
PubMed: https://pubmed.ncbi.nlm.nih.gov/41994924/

Paper 4: Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings
PubMed: https://pubmed.ncbi.nlm.nih.gov/41993946/

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I did something I've never done before for this episode — I went live from the middle of a national park. This is DigiPath Digest #42, broadcasting from the Great Sand Dunes National Park in Colorado via Starlink from my family road trip. Yes, it actually worked. And so did the papers.
This episode covers four papers that all ask the same uncomfortable question from different angles: how close is AI to being genuinely useful in real pathology practice — and what's still standing in the way? From LLMs interpreting cervical Pap smears, to AI guiding breast cancer treatment decisions from a simple H&E slide, to a practical roadmap for bringing generative AI into oncology workflows — this one covers a lot of ground.
I also introduced something new: my AI-powered paper summary podcast subscription. For $7 a month, AI hosts summarize digital pathology literature in a journal-club style so you can stay current without spending hours reading abstracts. I walk through how it works and why I built it.
What we cover:
[00:00] Going live from the wilderness — Starlink, sand dunes, and a very cold morning
[02:01] How I use AI-generated audio summaries to prep for each DigiPath Digest
[03:19] Paper 1: Can LLMs like ChatGPT and Gemini interpret cervical cytology? Spoiler: ~47–48% exact concordance — promising, but not there yet
[10:23] Bonus: My new AI-powered paper summary subscription — $7/month, journal-club style
[14:05] Paper 2: AI in oral oncology — CNNs for early lesion detection, multimodal prognostics, and the real barriers still blocking clinical adoption
[20:28] Paper 3: Generative AI in oncology — from chat tools to agentic EHR-integrated assistants, and why augmentation is the goal, not automation
[25:35] Paper 4: Computational pathology in breast cancer — predicting BRCA1/2, HER2, Oncotype DX, and treatment response from standard H&E slides
[31:39] Final thought: the floor just got raised for all of us — how I think about new technology in pathology
Resources & Links:
Paper 1 – LLMs & Cervical Cytology (PubMed): https://pubmed.ncbi.nlm.nih.gov/41931983/
Paper 2 – AI in Oral Oncology (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930554/
Paper 3 – Generative AI in Oncology Practice (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930309/
Paper 4 – AI & Digital Pathology in Breast Cancer (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930306/
Watch on YouTube: https://www.youtube.com/live/O2hOU4gM0Bk?si=oH8iJ8HiBb29USG3
Digital Pathology Place: https://www.digitalpathologyplace.com
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You don't need a fancy scanner, a huge budget, or a computational background to get started in digital pathology. That's what I told the ACVP podcast — and I meant it. In this episode, I share my full digital pathology journey: from being completely intimidated by scanners during residency, to building a career that combines toxicologic pathology, image analysis, and remote work at a global CRO.
If you're a resident, a trainee, or even a seasoned pathologist who hasn't fully stepped into the digital space yet — this one's for you.
We talked about practical ways to get started, what foundation models actually mean for our daily work, how to build a team when implementing digital pathology at your institution, and why change management might be the most underestimated skill in this whole process.
What we cover:
[00:00] My background — from veterinary school in Poland to digital pathology
[03:22] Why I chose industry over academia, and what that transition looked like
[05:02] How a simple IHC side project became my entry point into digital pathology
[07:11] How digital slides helped me pass my boards — and fall back in love with histopathology
[10:24] My first job at a digital pathology image analysis company
[12:00] What my current role at Charles River Laboratories looks like day-to-day
[13:53] The best free resources for trainees to start exploring digital slides RIGHT NOW
[15:26] Why pathologists need to understand image analysis principles — segmentation, classification, object detection
[19:31] Foundation models, transformer architecture, and why annotation bottlenecks may soon be a thing of the past
[24:13] Practical advice for institutions implementing digital pathology — equipment, teams, and managing resistance to change
[27:30] How I unplug: trail running, weight training, and pathology-themed earrings
Resources & Links:
Joint Pathology Center (JPC) digital slides: https://www.jpc.org
Davis Thompson Foundation — Noah Slidebox: https://www.davisthomasonfoundation.org
QuPath (free, open-source image analysis): https://qupath.github.io
Digital Pathology Place: https://www.digitalpathologyplace.com
Watch the full conversation on YouTube: https://youtu.be/wTDdlxJzq-A?si=xkz5YNljrUX5Snhd
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How close is pathology AI to making decisions that matter in real workflows, real trials, and real patient care?
In this episode of DigiPath Digest, I review five recent papers that approach that question from very different angles. We look at multimodal survival prediction in cervical cancer, pathology-driven response assessment in neoadjuvant immunotherapy for head and neck squamous cell carcinoma, AI-assisted Ki-67 scoring in pulmonary neuroendocrine neoplasms, automation and AI in hematologic diagnostics, and AI-based qFibrosis readouts from the Phase 3 MAESTRO-NASH trial.
What I liked about this set of papers is that they do not all tell the same story. Some show clear progress. Some show where AI already works well as an adjunct. Others make it very clear that validation, governance, reproducibility, and workflow design still matter just as much as model performance.
Key topics and timestamps
00:00 Introduction, Easter edition, and community updates 
00:51 USCAP recap, signed book giveaway, and free Digital Pathology 101 PDF 
02:04 Partnerships, lab automation preview, and what’s coming in this episode 
03:25 Multimodal deep learning for cervical cancer survival prediction 
13:00 Why pathology may be a better response endpoint than radiology in neoadjuvant HNSCC immunotherapy 
23:09 Ki-67 scoring in pulmonary neuroendocrine neoplasms: pathologists vs two AI systems 
33:46 AI, digital morphology, and automation in hematologic diagnostics 
43:29 qFibrosis, digital biomarkers, and the MAESTRO-NASH Phase 3 trial 
51:57 Closing thoughts, community updates, and Easter promotion 
Resources
 Deep Learning Can Predict the Overall Survival of Cervical Cancer Based on Histopathological Image, Gene Mutation and Clinical Information
 https://pubmed.ncbi.nlm.nih.gov/41902378/

 Modern Pathology-Driven Strategies in Neoadjuvant Immunotherapy for Head and Neck Squamous Cell Carcinoma: From Residual Tumor Quantification to Spatial and AI-Based Biomarkers
 https://pubmed.ncbi.nlm.nih.gov/41899621/

 Ki-67 Proliferation Index in Pulmonary Neuroendocrine Neoplasms: Interobserver Agreement Among Pathologists and Comparison of Two Artificial Intelligence-Based Image Analysis Systems
 https://pubmed.ncbi.nlm.nih.gov/41898274/

 Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations
 https://pubmed.ncbi.nlm.nih.gov/41897649/

 Quantitative regression of qFibrosis with resmetirom: Exploratory histologic endpoints from the MAESTRO-NASH phase III clinical trial
 https://pubmed.ncbi.nlm.nih.gov/41895606/
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Why do digital pathology projects get harder once the real workflow starts?
In this USCAP 2026 conversation, I talk with Robert Moody from Hamamatsu and Jake Eden from Agilent about what the conference theme, MAKING CONNECTIONS, looks like in actual digital pathology implementation. This was not just a conversation about products. It was a conversation about workflow. We talked about why consistent staining matters before scanning, why strong partnerships need a shared vision, and why labs increasingly want a simpler point of contact as they move into digital pathology.  
One point I really liked is that the value of a partnership is no longer just in combining components. It is in reducing complexity for the lab. Robert and Jake explain how vendors increasingly act as guides during digital transformation, helping customers navigate technical decisions, implementation steps, and the many stakeholders involved beyond pathology itself. That includes IT, information security, legal, finance, and lab operations. 
Another key theme is that no two deployments look the same. Some labs are centralized. Some are hub-and-spoke. Some outsource parts of the workflow. That is why future-proofing came up so strongly in this episode. Jake talks about keeping options open with open, agnostic workflows, and Robert makes the practical point that the most expensive thing you can do is the same implementation twice. 
Key highlights
[00:22] Why this episode moves from high-level partnerships to what they look like in the lab 
[02:33] Why staining consistency matters for successful digital workflows 
[03:14] Shared vision, relationships, and why partnerships start with people 
[05:29] The idea of a single point of contact to reduce complexity for labs 
[08:32] Why vendors have become digital pathology guides 
[10:03] Why every deployment is unique 
[14:22] Future-proofing and choosing open, agnostic workflows 
[15:46] Why doing the same implementation twice is the expensive mistake to avoid 
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Get the "Digital Pathology 101" FREE E-book and join us!