Digital Pathology Podcast

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Can AI copilots really keep up with pathologists when the cases are new, the workflow is messy, and the benchmark is actually protected from leakage?
In this episode of DigiPath Digest #48, I focus on one paper: DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset. I chose this paper because I think the field needs more of this kind of work. Less hype. More evaluation. Less “look what AI can do.” More “how do we test it in a way that actually means something?” 
In this session, I look at what makes DALPHIN important for pathologists, lab leaders, and digital pathology trailblazers trying to make sense of pathology AI right now. The paper benchmarks three models against human pathologists: two general-purpose models, Gemini 2.5 Pro and GPT-5, and one pathology-specific model, PathChat+. The dataset includes 1,236 images from 300 cases, covering 130 diagnoses, 14 pathology subspecialties, and cases from six countries. Human performance is benchmarked with 31 pathologists from 10 countries. 
What I like about this paper is that it does not stop at top-line performance. It deals with the benchmarking problem itself. The authors built a sequestered, indirectly accessible ground truth so the evaluation data could not simply be scraped into model training. That matters because without that protection, benchmarking can become an illusion of genius rather than a real test of generalization. 
The results are interesting and more nuanced than a simple win-or-lose story. PathChat+ reached expert-level performance in four of six tasks, Gemini in two of six, and GPT in one of six. That tells us something important already: pathology-specific training matters. But it also does not mean pathology is solved. In organ recognition, expert pathologists still outperformed all the models. In rare cancers, none of the models reached expert-level performance. And in ambiguous cases, the models still struggled with something human pathologists do all the time: expressing uncertainty. 
I also spend time on one of the most practical parts of the paper: model behavior. Gemini tended to overcall. GPT tended to undercall. PathChat was more balanced. That matters in practice. A pathologist using a copilot needs to know the tool’s calibration bias before they can safely interpret what it is telling them. I also talk about anchoring bias in conversational interfaces, where early hallucinations can propagate through later answers if memory is not reset between questions. That is not just a technical curiosity. That is a workflow and safety issue. 
Why should you listen? Because this episode is really about a bigger question: What kind of evidence should pathologists demand before AI copilots enter real workflows? If you want to understand validation, data leakage, rare-case performance, uncertainty, and why these tools should still be treated as co-pilots rather than autopilots, this is a useful paper to know. 
Episode Highlights
01:20 – Why I chose the DALPHIN preprint and why benchmarking matters right now. 
05:38 – What is in the DALPHIN dataset: 300 cases, 130 diagnoses, 14 subspecialties, 6 countries. 
07:57 – Top-line performance: PathChat+ reaches expert-level performance in 4 of 6 tasks. 
09:41 – The benchmarking trap of data leakage and why DALPHIN’s sequestered ground truth matters. 
12:19 – Why real pathology diagnosis is not text-only and why macro + micro context matters. 
15:26 – Tissue recognition, neoplasm detection, ambiguity, and conversational memory: how the testing was structured. 
21:29 – The diagnostic personalities of the models: overcalling, undercalling, and balanced behavior. 
24:36 – Rare cancers: where AI copilots still fall short of expert human performance. 
28:00 – Why binary outputs are not enough when pathology often lives in uncertainty. 
31:37 – Anchoring bias and conversational memory: how early hallucinations can keep propagating. 
37:11 – Why these tools should be treated as co-pilots, not autopilots. 
40:29 – Resources for beginners: Digital Pathology 101 and continued AI literacy. 
Resources mentioned
DALPHIN preprint: arXiv:2605.03544v1 
DALPHIN evaluation platform: dalphin.grand-challenge.org 
PathChat+ pathology-specific AI model discussed in the benchmark. 
Digital Pathology 101 free eBook by Dr. Aleksandra Zuraw. 
Educational streams on tissue recognition and computer vision literacy mentioned in the session.
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Do you really need a scanner, whole slide images, and AI infrastructure before you can start in digital pathology?
In this episode, I argue that you do not.
I’m Dr. Aleksandra Zuraw, veterinary pathologist and digital pathology educator, and this talk is about a belief I hear all the time: I don’t have the tools yet, so there is no point learning digital pathology. I used to think that too. When I was training in Berlin, there was one Leica 6-slide scanner, and it felt like digital pathology was only for a small group of chosen people. That experience made the field feel distant, exclusive, and not really available to beginners. 
What changed for me was not a new scanner. It was a small project.
I needed a more consistent way to quantify a senescence marker in archived skin samples, so I used a microscope camera, captured images, opened them in Microsoft Paint, and manually marked cells with colored dots. It was scrappy. Very low tech. But it was also digital, consistent, and verifiable. That project became my first real step into digital pathology and helped me get my first job in the field, where I worked between pathologists and image analysis scientists on biomarker quantification and patient stratification problems. 
That is the core point of this episode: knowledge unlocks technology.
Scanners matter. AI tools matter. But the deeper bottleneck is whether enough people understand how to use these tools, ask good questions, and connect pathology expertise with digital workflows. That is why this episode is really about readiness. Not readiness of the hardware. Readiness of the people.
I also talk about Dr. Taladzer from Pakistan, whose story makes this point even more clearly. At the time, Pakistan had around 220 million people, about 500 pathologists, and zero scanners. She still started learning digital pathology during COVID using a microscope and camera, joined the Digital Pathology Association, taught herself from papers and online resources, and kept going even after multiple AI vendors rejected her because she did not have whole slide images. Eventually, she found a DIY image analysis platform, learned to annotate and train models on static images, completed projects quickly, and went on to publish more than 10 digital pathology papers without ever using WSI.
Why should you listen?
Because this episode is for pathologists and lab leaders who are interested in digital pathology but still feel stuck at the beginning. It is for people waiting for permission, perfect infrastructure, or a formal roadmap. And it is for trailblazers who came back from a meeting or conference energized, but need a practical way to turn that energy into action before it fades.
I also address an important AI question near the end: How do we know an AI model is good enough for pathology? I talk about why models are only as good as the pathologist annotations used to train them, why concordance between pathologists matters, how orthogonal labels like IHC can improve model quality, and why pathologists still need to stay in the loop as these systems develop and get deployed.
If you are trying to figure out where to start, this episode gives you a practical answer: start where you are. Start with what you have. Start learning now.
Episode Highlights
00:00 – Why the real barrier to digital pathology is usually not the hardware
00:33 – What it feels like to be at the beginning of the digital pathology journey
02:50 – My first practical digital pathology project using a microscope camera and Microsoft Paint
05:37 – How that low-tech project led to my first digital pathology job
08:52 – Why knowledge, not infrastructure, is the real unlock
09:57 – Dr. Taladzer’s story: starting digital pathology in Pakistan with zero scanners
12:03 – What happened after repeated vendor rejection and why persistence mattered
14:39 – The “forgetting loop” vs the “commitment loop” after conferences
16:48 – Practical next steps: book, PubMed alerts, journal clubs, webinars, vendor resources
18:52 – Why I believe digital pathology is the gateway to faster diagnosis
20:00 – How to think about whether an AI model is really ready for pathology
Resources Mentioned
Digital Pathology 101 – free book recommended as a starting point for learning digital pathology. 
Digital Pathology Association – mentioned as a learning resource and professional community. 
PubMed alerts for AI and digital pathology. 
Journal clubs – mentioned as one way to keep learning consistently. 
Webinars and vendor resources – suggested as practical ways to keep building knowledge. 
A4A – the DIY image analysis platform that supported Dr. Taladzer’s early work with static image annotation and model training. 
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Why are pathology vendors still speaking different image languages when radiology solved that problem decades ago?
In this episode of DigiPath Digest #46, I talk through four papers that all point to a bigger issue in digital pathology: we are not only dealing with better algorithms. We are dealing with interoperability, workflow design, explainability, and whether the field is actually ready to use these tools well.
I start with DICOM in digital pathology, because I think this is still one of the most important infrastructure questions in the field. Digital pathology has clear value for consultation, image analysis, archival, and workflow, but vendor-specific whole slide image formats still create silos. In the episode, I explain why DICOM matters, why adoption is still low, how the multi-resolution pyramid works, and why this is really about enterprise imaging and future-proofing, not just file conversion. 
Then I move into kidney transplant rejection, where the paper makes a strong case for multimodal precision diagnostics. Creatinine is late. Antibody testing can miss important biology. Biopsies can miss the area that matters. So the opportunity is not to replace pathology, but to combine biomarkers, biopsy, and machine learning in a way that is more useful than any one signal alone. I also talk about explainability here, because if a model gives a risk score, we need to know what contributed to it. 
The third paper focuses on perineural invasion in solid tumors, and I liked this one a lot because it shows how AI can help standardize something that is clinically important but still inconsistently detected and reported. Perineural invasion is not just a passive pathway of spread. The biology is more active than that, and the quantification can go far beyond a simple yes-or-no answer. This is a good example of where digital pathology can do something humans cannot realistically do by eye at scale. 
The last paper is on gastric cancer immunohistochemistry biomarkers and advanced quantification, including HER2, PD-L1, mismatch repair, and CLDN18.2. This section is really about complexity. We are now asking pathologists to visually score biology that is getting harder and harder to summarize consistently, especially when markers, spatial context, and multiplexing all start to matter at once. I make the case that computational pathology is becoming necessary here, not because pathologists are failing, but because the biology is outgrowing purely visual workflows. 
What ties these four papers together is simple: digital pathology is not only about remote reading anymore. It is about interoperability, quantification, explainable AI, and making pathology more precise in places where the old workflow is reaching its limit. If you are a pathologist, lab leader, or digital pathology trailblazer trying to figure out what actually matters right now, this episode will help you connect the dots.
Episode Highlights
 07:41 – Why DICOM still matters if we want digital pathology systems to work together.
14:39 – Current adoption of SVS, MRXS, and DICOM, and why DICOM is still lagging.
16:44 – How the DICOM whole slide image pyramid works and why it matters for workflow.
24:29 – Why kidney transplant rejection is still difficult to diagnose with any single marker.
29:18 – Why perineural invasion is clinically important and still inconsistently reported.
34:44 – How AI can quantify tumor-nerve relationships more consistently than visual review alone.
46:39 – Why gastric cancer biomarker scoring is getting too complex for purely visual workflows.
54:55 – Multiplexing, spatial biology, and why explainable AI matters in biomarker interpretation.
01:04:01 – What is really blocking digital pathology adoption: cost, workflow, regulation, or mindset? 
Resources mentioned
DICOM / digital pathology interoperability paper
https://pubmed.ncbi.nlm.nih.gov/42093730/
Kidney transplant rejection, biomarkers, and artificial intelligence
https://pubmed.ncbi.nlm.nih.gov/42073482/
Perineural invasion in solid tumors with AI and machine learning applications
https://pubmed.ncbi.nlm.nih.gov/42100436/
Gastric cancer IHC biomarkers, advanced detection methods, and perspectives
https://pubmed.ncbi.nlm.nih.gov/42075555/
Digital Pathology Place
https://digitalpathologyplace.com
Digital Pathology 101
Free PDF book mentioned at the end of the episode through Digital Pathology Place.
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What if the most frightening part of a pathology report is not the word cancer, but the silence that follows?
In this episode of the Digital Pathology Podcast, Dr. Aleksandra Zuraw talks with Michele Mitchell—breast cancer survivor, caregiver, national patient advocate, and longtime volunteer across Michigan Medicine, ASCP, the Digital Pathology Association, and MyPathologyReport.ca—about what happened when she saw her own cancer slide years after treatment. That moment changed how she understood her disease, her risk, and her role as a patient advocate.
This is not just a patient story. It is a digital pathology implementation story.
The episode looks at how digital pathology removes practical barriers to sharing slides, why pathology clinics matter, and what becomes possible when pathologists move from being hidden in the background to becoming direct contributors to patient understanding. Michelle and Dr. Aleks talk through the communication gap around pathology reports, the emotional cost of delayed explanation, and the real-world workflow of pathology clinic visits built to help patients review their slides with the pathologist who made the diagnosis.
They also discuss what the 21st Century Cures Act changed for patients, why immediate access to reports without interpretation can still create fear, and how pathology clinics can bridge the gap between raw data and real understanding. The conversation gets practical too: how patients can request a pathology clinic visit, what virtual pathology consults can look like, how billing and workflow concerns are already being addressed, and why the infrastructure question is smaller than many people assume.
If you work in digital pathology, pathology informatics, patient communication, or implementation, this episode is a reminder that visibility is not extra. It is part of the value proposition. And for pathologists who worry this is too far outside the traditional role, the episode offers a grounded counterpoint: the workflows, templates, billing structures, and virtual options already exist.
Highlights
00:00 – Why pathology needs to become more patient-centered
Michele frames the core problem clearly: what often scares patients is not only cancer, but the silence around the diagnosis. 
00:34 – How digital pathology changes the patient experience
Digital slides make it possible for patients to see their diagnosis, compare normal and abnormal tissue, and ask better questions. 
11:13 – What happened when Michele saw her cancer for the first time
More than a decade after treatment, seeing her own slide changed how she understood her grade, her risk, and her daily health decisions. 
16:19 – Why visual pathology can change adherence and lifestyle
Michele explains how the image-based explanation became a practical turning point, not just an emotional one. 
20:43 – The case for direct pathologist-patient communication
The episode reviews why this can improve clarity, treatment understanding, clinic efficiency, and even professional satisfaction for pathologists. 
38:40 – What a pathology clinic actually looks like
From preparation and consent to slide review, plain language, empathy, and follow-up, the workflow is much more concrete than many people assume. 
45:35 – ASCP’s certification workshop for pathology clinics
Michele describes the national effort to make pathology clinics reproducible, scalable, and easier to implement. 
49:32 – What the 21st Century Cures Act changed
Patients now get near real-time access to reports, but that access still needs interpretation, context, and support. 
01:03:23 – Pushback, logistics, and why the barriers are not where people think
Time, reimbursement, scheduling, and virtual setup are addressed directly with examples already in practice. 
01:16:57 – The future: patient-friendly reports, AI, and pathology as part of the care team
The episode closes on a practical vision: not hype, but tools and workflows that already exist and can be connected now. 
Resources mentioned
Digital Pathology Place – website and educational platform referenced by Dr. Aleks as the home for her work and resources. 
Digital Pathology 101 – Dr. Aleks’s book, referenced in the broader discussion of patient and pathologist education. 
Michigan Medicine breast pathology clinic – launched in 2023 as a patient-facing breast pathology clinic model. 
ASCP pathology clinic certification workshop – national workshop co-developed to help institutions build pathology clinics. 
21st Century Cures Act – legal framework behind near real-time patient access to pathology reports and related health data. 
MyPathologyReport.ca – patient-friendly pathology education resource reviewed with patient advocate involvement. 
American Cancer Society Reach to Recovery – support resource mentioned for breast cancer patients. 
Scanslated – patient-friendly report interface discussed as part of a future-facing model for pathology communication. 
Virtual pathology consults/telehealth setup – discussed as a scalable way to lower implementation friction.
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DigiPath Digest #45 asks a practical question: can AI in pathology move from correlation to real clinical use? In this episode, I review four papers that push on that question from different angles: computational pathology moving toward morphology-driven molecular inference, the current state of digital cytopathology and AI, multi-omics and precision oncology in hepatocellular carcinoma, and AI literacy in veterinary education. What ties them together is not model performance alone. It is the harder question of validation, workflow fit, quantitative use, ethics, and human oversight.
In the first paper, I talk about computational pathology as more than pattern recognition. The focus is on morphology-driven molecular inference, digital biomarkers, and why spatial omics matters as biological ground truth. I also discuss why continuous quantitative scoring is more useful than forcing biology into rough scoring buckets. 
The second paper focuses on digital cytopathology. Cytology was early for FDA-cleared AI in cervical screening, but non-gynecologic cytology is still much harder to digitize because of specimen variability and workflow complexity. I also cover telecytology, rapid onsite evaluation, automation, and quality control. 
The third paper looks at hepatocellular carcinoma and AI-driven precision oncology. This part is about using AI and machine learning to integrate genomics, transcriptomics, proteomics, metabolomics, radiomics, and pathology to support biomarker discovery, tumor microenvironment analysis, and treatment stratification. 
The fourth paper may be the most broadly useful. It proposes an AI literacy curriculum for veterinary education that covers AI fundamentals, machine learning evaluation, LLMs, ethics, liability, and academic integrity. I think that matters far beyond veterinary medicine, because if clinicians are expected to use AI tools responsibly, AI literacy cannot stay optional. 
Highlights
00:01 Welcome and overview of the four papers
03:02 Computational pathology and morphology-driven molecular inference
11:01 Digital cytopathology, telecytology, and QC
20:47 AI/ML in hepatocellular carcinoma precision oncology
31:04 AI literacy in veterinary education
47:42 Final takeaways and Digital Pathology 101 update 
Resources
Computational Pathology as a Mechanistic Discipline: From Morphology to Molecular Data
https://pubmed.ncbi.nlm.nih.gov/42052846/

Advances in Digital Cytopathology and Artificial Intelligence Applications
https://pubmed.ncbi.nlm.nih.gov/42046894/

Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology
https://pubmed.ncbi.nlm.nih.gov/42065059/

Curriculum Framework for Artificial Intelligence Literacy in Veterinary Education
Front Vet Sci. 2026;13:1801756 
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