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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

1. 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/
   
   
2. 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/
   
   
3. 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/
   
   
4. Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations
   https://pubmed.ncbi.nlm.nih.gov/41897649/
   
   
5. 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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Get the "Digital Pathology 101" FREE E-book and join us!