Digital Pathology Podcast

Hosted by Aleksandra Zuraw, DVM, PhD

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245

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Jul 2026

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EN-US

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Aleksandra Zuraw from Digital Pathology Place discusses digital pathology from the basic concepts to the newest developments, including image analysis and artificial intelligence. She reviews scientific literature and together with her guests discusses the current industry and research digital pathology trends.

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August 17, 2026Episode 24126 min

241: Screening Efficiency Over Experience: Rethinking Cytology Expertise

Send us Fan MailDoes more experience automatically make a cytotechnologist more accurate—or does where they look first matter more?In DigiPath Digest #50, I review a digital cytology eye-tracking study that challenges the assumption that diagnostic accuracy improves steadily with years of practice.The researchers tracked the visual behavior of 100 board-certified cytotechnologists with 1 to 40 years of experience. They found no statistically significant linear relationship between years of experience and diagnostic accuracy. Instead, low-power field efficiency—the ability to identify an important target quickly within a wider field—emerged as the key predictor of high accuracy discussed in the study.The study also examined whether this visual skill can be developed. Twenty-eight students completed an intensive three-month cytotechnology training program. After training, they located diagnostic targets more quickly and spent less attention on normal, nondiagnostic cells. In other words, they learned both where to look and what to disregard.What could this mean for digital pathology education?As AI-assisted workflows take on more of the exhaustive searching, cytotechnologists and pathologists may increasingly work as expert verifiers. That requires rapid target assessment, strong knowledge of normal morphology, and awareness of risks such as confirmation bias and cognitive fatigue.The study has an important limitation: it used static images rather than dynamic whole slide imaging. The findings raise useful questions about visual expertise, training, and competency assessment, but they shouldn’t be generalized beyond the study design without further research.Episode Highlights00:00 – Welcome to DigiPath Digest #50 and introduction to the paper04:10 – Why the traditional definition of professional expertise is changing07:02 – Moving from exhaustive searching to verification in AI-assisted workflows09:04 – How eye tracking was used with 100 board-certified professionals10:25 – Years of experience versus diagnostic accuracy13:13 – Experience-based caution and attention to sample information15:16 – Low-power field efficiency as a predictor of high accuracy17:05 – Practical low-power field demonstration using a whole slide image20:15 – Searching versus detecting and the mental map of normal morphology24:04 – Comparing high- and low-performer visual scan paths25:27 – Cognitive filtering: knowing what not to examine27:35 – Can visual efficiency be taught in three months?29:55 – How AI may shift the human role from searcher to verifier30:38 – Study limitations: static images versus dynamic whole slide imaging32:37 – Could gaze efficiency influence future competency assessment?33:44 – Digital pathology learning resources and closing thoughtsResources MentionedAbstract and paper: Screening Efficiency Over Experience: Rapid Target Detection in Low-Power Field as a Modifiable Cognitive Biomarker for Diagnostic Accuracy in Digital CytologyListen to the full DigiPath Digest #50 recording to examine what the study found, what it didn’t prove, and how visual search skills could influence digital cytology training.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

August 13, 2026Episode 2401 hr 4 min

240: Computational Pathology Is Changing Companion Diagnostics

Send us Fan MailCan a treatment decision depend on whether one pathologist sees 45% biomarker positivity and another sees 55%?Visual immunohistochemistry scoring helped establish precision oncology. But as targeted therapies become more sensitive to subtle biological differences, categorical scores such as 0, 1+, 2+, and 3+ may no longer capture the information needed to identify the right patients.In this episode, I speak with three Roche experts:Gordana Juric-Sekhar, MD, anatomic pathologistSaleh Miri, PhD, Director of Digital Pathology AI AlgorithmsPurvi Gaglani, Regulatory Affairs Lead for Digital PathologyWe discuss how computational pathology is changing companion diagnostics by moving biomarker assessment from visual estimates to continuous, cell-level measurements.The conversation examines the limitations of manual IHC scoring, including interobserver variability, intraobserver variability, visual fatigue, borderline cases, tumor heterogeneity, and the inability of the human eye to measure complex spatial relationships.Using TROP2 scoring in advanced non-small cell lung cancer as an example, Saleh explains the normalized membrane ratio. This computational metric measures protein expression at the cell membrane relative to total expression within the cell—something that can’t be reproduced through conventional visual scoring.We also clarify the difference between computer-assisted scoring and a fully computational companion diagnostic. An assisted tool supports a pathologist’s visual interpretation. A computational CDx generates the biomarker measurement through algorithmic, cell-level analysis.That doesn’t remove the pathologist.Pathologists remain responsible for evaluating tissue quality, staining quality, scan quality, tumor selection, image analysis results, and the final clinical context. They can reject a stain, request a rescan, exclude inappropriate regions, question the result, or seek a second opinion.The episode also examines the regulatory implications of computational companion diagnostics. Instead of evaluating a single IHC assay, regulators may need to assess the complete system - from tissue preparation and staining to scanning, image management, algorithmic analysis, display, and the final biomarker report.Finally, we discuss what laboratories will need to implement these workflows, including validated scanning infrastructure, cybersecurity, tighter preanalytical process control, and training that helps pathologists interpret continuous computational measurements.Episode Highlights00:00 — Pathologists remain central to computational CDxWhy computational tools provide more precise measurements without replacing pathology expertise.01:09 — Why companion diagnostics are changingVisual IHC scoring helped launch precision oncology, but the model is approaching its limits.04:53 — The current companion diagnostic landscapeHow IHC, next-generation sequencing, liquid biopsy, and visual biomarker scoring are used today.07:01 — The mathematical burden placed on the human eyeWhy manually assessing tens of thousands of tumor cells requires pathologists to estimate rather than calculate.08:39 — The borderline patient dilemmaA digital tool can distinguish measurements such as 74% and 76%, while that difference is difficult to reproduce visually.09:27 — Why spatial context mattersComputational pathology can measure biomarker heterogeneity, clustering, and relationships between tumor and immune cells.12:17 — Where manual scoring reaches its limitsInterobserver variability, intraobserver variability, fatigue, staining interpretation, and heterogeneous tumors.18:29 — Moving from judgment calls to quantified measurementsWhy the next stage of precision oncology requires information beyond human visual perception.19:14 — Computer-assisted scoring versus computational CDxThe important distinction between helping a pathologist calculate an existing score and generating a new algorithmic measurement.23:22 — Computational pathology and decentralized workflowsHow digital images can support remote review, access to expertise, and second opinions.27:48 — Why therapies require higher-resolution biomarkersModern targeted treatments may respond to biological differences that categorical scoring can’t capture.32:09 — TROP2 in advanced non-small cell lung cancerThe episode’s example of a biomarker requiring computational measurement.33:26 — Understanding the normalized membrane ratioHow the algorithm measures membrane expression relative to total protein expression at the individual-cell level.35:46 — Working with regulators on a new diagnostic modelPurvi discusses global health authority engagement and the FDA Breakthrough Device Designation.38:33 — The computational CDx as a system of systemsWhy staining, scanning, image management, algorithms, displays, and reporting must be evaluated together.40:11 — Changes to validated workflow componentsHow using a different scanner, monitor, or other component could fall outside the defined device configuration.43:30 — Why computational pathology is becoming necessaryContinuous measurements can reveal biomarker-treatment relationships that may remain hidden within categorical scores.49:12 — The pathologist’s role in the workflowReviewing sample, staining, scan, image, algorithmic analysis, and the final biomarker result.53:39 — Digital second opinionsHow image management systems can simplify collaboration without physically transporting glass slides.56:43 — What laboratories need to prepareValidated infrastructure, cybersecurity, preanalytical control, training, and digital pathology literacy.58:49 — Learning to interpret computational resultsThe shift from visually estimated categories to continuous, quantitative biomarker measurements.Resources MentionedFull discussion on YouTube: https://youtu.be/oKW1xC6TTZgListen to the full discussion to understand how computational pathology could change companion diagnostics—and what pathologists, laboratories, and regulators must prepare for next.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

July 29, 2026Episode 24542 min

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!

July 22, 2026Episode 2441 hr 35 min

244: Why AI Still Hasn't Revolutionized Drug Discovery (Yet) | Thibault Geoui, PhD

Send us Fan MailIf AI is already being used across the drug development pipeline, why hasn’t its impact matched the investment?AI can help researchers review scientific literature, predict protein structures, prioritize molecules, assess toxicity, support clinical trials, and monitor adverse events. But access to better tools doesn’t automatically create better drugs.In this episode, I speak with Thibault Geoui, Science CDO and host of the Tech & Drugs Podcast, about where AI is making a practical difference in drug discovery and development—and where the results remain limited. We map AI across the full drug development funnel, from basic research and target identification to preclinical testing, clinical trials, regulatory documentation, commercialization, and pharmacovigilance.We also discuss why digital-native tech-bio companies may be better positioned to benefit from AI than traditional pharmaceutical organizations. The difference isn’t simply the model. It’s how data, people, laboratory experiments, and AI tools are connected inside the workflow.For digital pathology professionals, the conversation becomes especially relevant when we examine AI-powered biomarker development, the role of pathology in pharmaceutical research, and the Roche–PathAI case discussed in the episode.And, of course, we talk about the problem every AI user eventually faces: an answer can look polished, specific, and completely convincing—and still be wrong.Episode Highlights00:00 — When convincing AI output creates more work Why AI can accelerate information generation while increasing the time required for review and verification.02:15 — From structural biology to science and technology leadership Thibault shares his background in X-ray crystallography, structural biology, scientific data, and digital product development.15:36 — Understanding the drug discovery and development funnel How thousands of potential compounds are narrowed down through discovery, preclinical research, clinical trials, and approval.20:00 — AI for scientific literature review How alerts, filtering, summarization, and information extraction can help researchers manage a rapidly growing scientific literature base.22:32 — AlphaFold and protein structure prediction What faster access to predicted protein structures changes for researchers—and why structural prediction alone doesn’t solve drug discovery.24:13 — Searching an enormous chemical space How AI can help design and prioritize potential molecules for synthesis and experimental testing.25:50 — Predicting efficacy and toxicity Where AI supports preclinical research, why the models remain imperfect, and why experimental validation still matters.29:38 — Has AI changed drug development outcomes yet? A practical discussion about drug approval rates, AI investment, uneven returns, and the difference between deploying a tool and integrating it into a process.34:33 — Why traditional pharma struggles to scale AI Siloed data, legacy systems, organizational complexity, and the need to build reusable data workflows.37:57 — The “lab in the loop” model How tech-bio companies connect AI predictions with wet-lab experiments and feed the new data back into their models.44:37 — Can tech-bio companies shorten development timelines? How digital-native organizations are changing parts of the discovery and preclinical process.58:00 — AI, pharma, and digital pathology What the Roche–PathAI case discussed in the episode may indicate about the role of pathology data, biomarker discovery, and pharmaceutical workflows.01:06:17 — AI errors in regulated environments Why responsibility remains with the person or company submitting AI-assisted work, regardless of which tool produced it.01:17:37 — The growing cost of AI tools Subscriptions, token limits, model selection, AI orchestrators, and the need to use expensive tools more intentionally.01:27:50 — What successful AI adoption requires Starting with focused pilots, training scientists and technologists together, and treating implementation as organizational change.01:30:26 — The AI quirks that still frustrate users Hallucinated information, ignored writing instructions, stylistic habits, and poor awareness of time and context.The episode’s timestamped themes and examples are documented in the supplied summary. The broader discussion covers AI from literature mining and molecular design through clinical development and post-market monitoring. Resources Mentioned Thibault Geoui’s LinkedIn profile Tech & Drugs PodcastMIT NANDA study on generative AI implementation and return on investment Insilico Medicine as an example of a digital-native tech-bio company AI is already changing how scientific work gets done. The bigger question is whether organizations can redesign their workflows, train their teams, and maintain the human oversight needed to use it well.Listen to the full episode for a practical look at AI in drug discovery, drug development, and digital pathology.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

July 2, 2026Episode 24342 min

243: How to Teach AI to Healthcare Professionals | Podcast with Candice Chu

Send us Fan MailWhat does AI literacy actually look like for pathologists, researchers, and future clinicians? And how do you teach it in a way that is practical, not abstract?In this episode, I talk with Candice Chu, DVM, PhD about something I think a lot of people in digital pathology and computational pathology are feeling right now: AI is moving fast, but education is still catching up.Candice is a clinical pathologist, veterinarian, and educator building AI-focused teaching and research at Texas A&M. We worked together before on digital pathology and image analysis projects, so this conversation felt especially grounded. We talk about her AI literacy curriculum framework for veterinary education, why she decided to build it, and what it takes to teach AI in a way that is useful, ethical, and realistic.This episode is about understanding what AI tools are good for, where they can waste your time, and why hands-on experience matters. Candice explains why she sees AI as a set of tools, not a belief system. Try them. Learn them. Keep what improves your workflow. Drop what does not.We also talk about the difference between putting educational content online and building formal institutional teaching. That matters because social media can move quickly, but curriculum changes, research, and professional organizations shape longer-term adoption. Candice shares how her course started as a low-stakes elective, then grew into a more structured framework that combines education with publishable research.A big part of this conversation is the curriculum itself. We go through what students actually learn: AI fundamentals without heavy math, machine learning and image analysis, large language models, prompt engineering, chatbot building, ethics, literature research, and final projects where students evaluate real tools and workflows. I liked that the course does not stop at theory. It asks students to use tools, question them, and explain where they help and where they do not.We also get into something that matters far beyond veterinary medicine: professional responsibility. If AI is involved in a workflow, the clinician is still responsible. That includes fabricated citations, bad outputs, weak prompts, and the temptation to trust tools too quickly. Candice makes a strong case that AI education needs ethics, legal context, and interdisciplinary teaching built in from the start.If you are trying to think more clearly about AI in pathology, education, workflow design, or professional training, this episode gives you a concrete example of what responsible AI literacy can look like.Episode Highlights00:00 – Why AI tools are just tools, and why trying them matters even if you later decide not to keep using them00:33 – Who Candice Chu is and why her work on AI literacy in veterinary medicine is worth paying attention to02:33 – Why going back to Texas A&M changed the scale of Candice’s AI research and teaching07:53 – How the AI course was designed as a low-stakes elective first, and why that helped student engagement11:16 – Where veterinary AI education stands now, and what professional organizations like ACVP are doing13:08 – Why AI adoption in veterinary medicine is still slow, and what skepticism usually sounds like in practice15:19 – Real examples of how Candice uses LLMs and computer vision in pathology, medical records, and research19:58 – What is actually inside the 15-week AI literacy curriculum, from fundamentals to final projects24:16 – Why ethics and legal responsibility are not optional in AI education31:35 – Why no-code tools and vibe coding are entering the curriculum already38:50 – The AI tools Candice is testing in her own workflow, including Claude, Codex, and PerplexityResources mentionedCandice Chu’s AI literacy curriculum framework paper in Frontiers in Veterinary ScienceCandice’s earlier work on ChatGPT in veterinary medicineTexas A&M and the institutional setting where Candice is building AI research and teachingMr. Don Riddick and the AVMA AI working group, mentioned in the ethics and legal contextClaude, Codex, and Perplexity as AI tools Candice is actively testingDigital Pathology 101, mentioned in the conversation as a teaching resourceCandice’s online educational work on Instagram.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

June 24, 2026Episode 24242 min

242: Foundation Models in Pathology: Strong on Paper, Ready for Labs?

Send us Fan MailAre pathology foundation models actually ready for labs, or are they still stronger on paper than in practice?In this episode of DigiPath Digest #49, I unpack a timely review on pathology foundation models and ask the question that matters most to me: not just what these models can do, but what has to be true before they are genuinely useful in real pathology workflows.I walk through how pathology AI moved from narrow, task-specific models into the era of transformer-based foundation models. That shift matters because pathology is no longer only about looking at H&E in isolation. Today, pathologists are expected to integrate morphology, immunohistochemistry, molecular assays, genomics, and clinical context. That growing complexity is one reason foundation models are getting so much attention.In this discussion, I explain how transformers entered pathology, why image patches are treated like tokens, and how shared embeddings can support classification, regression, segmentation, and multimodal retrieval. I also go through the major pathology foundation models mentioned in the paper, including Virchow/Virchow2, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, GigaPath, and TITAN, and why scale alone is not the full story.A big part of this episode is about the gap between benchmark performance and clinical readiness. I talk about the persistent limitations in training data diversity, the overuse of TCGA, and why public benchmarks can still miss what real pathology practice looks like. I also cover where foundation models still struggle, especially in cytopathology, hematopathology, and underrepresented disease areas, along with the real-world problems of artifacts, domain shift, concept drift, infrastructure burden, regulatory complexity, and workflow disruption.For me, one of the most important themes is this: AI in pathology should augment, not replace, pathologists. The future is not about handing diagnosis to a model. It is about building tools that support pathologists better, fit real workflows, and can be validated in ways that deserve trust.I also spend time on what comes next: explainable AI, counterfactual explanations, conversational interfaces, retrieval-augmented systems, multimodal fusion, and the need for deployment-centric validation rather than paper-only excitement.If you are trying to understand where pathology foundation models really stand today, this episode will help you separate the promise from the practical barriers.Episode Highlights00:01 – Why I chose this paper, what is changing at Digital Pathology Place, and why foundation models are worth paying attention to now.02:15 – The core questions: what pathology foundation models are, where they are, and how difficult they are to apply in pathology.04:50 – Why pathology is becoming more cognitively demanding, and how multimodal complexity is driving interest in scalable AI.07:02 – From narrow AI to transformers: how pathology moved beyond single-task CNN models.10:16 – How transformers work in pathology: image patches as tokens, self-attention, embeddings, and downstream tasks.14:16 – Why multimodality matters, and what kinds of data foundation models may eventually integrate.15:27 – Timeline of key model developments, from “Attention Is All You Need” to gigapixel-scale pathology foundation models.17:13 – The leading models and what scale really looks like: Virchow, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, and GigaPath.19:51 – Why dataset diversity matters more than sheer volume, and why TCGA is not enough.23:17 – Where foundation models still struggle: cytopathology, hematopathology, rare disease, artifacts, scanner shifts, and pen marks.28:06 – Explainability, counterfactual explanations, and why trust in pathology AI needs more than attention maps.30:17 – The real deployment hurdles: regulation, infrastructure, workflow fit, and economics.36:32 – Why AI should augment pathologists, not replace them, and which tedious tasks pathologists would gladly hand over.38:36 – Retrieval-augmented and conversational AI in pathology: where interactive systems may actually help.40:51 – Vision-language models and multimodal fusion with histology, radiology, genomics, and clinical notes.42:16 – The path forward: deployment-centric design, prospective multi-site validation, and human-AI collaboration.44:08 – Closing thoughts on AI literacy, community learning, and what needs to happen next.Resources MentionedMain paper discussed:Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspectivehttps://doi.org/10.3390/bioengineering13050577Review article / journal landing page:https://doi.org/10.3390/bioengineering13050577Benchmarks mentioned:PathoBench — discussed in the review paper; use the review link here for context until you want to swap in a canonical project page:https://doi.org/10.3390/bioengineering13050577PathBench — public benchmark paper:https://arxiv.org/abs/2505.20202MEDFAIR — benchmark paper:https://arxiv.org/abs/2210.01725MEDFAIR code repository:https://github.com/ys-zong/MEDFAIRModels mentioned:Model overview in the review (Virchow/Virchow2, UNI, CONCH, H-Optimus, GigaPath, TITAN, Mayo Clinic Atlas):https://doi.org/10.3390/bioengineering13050577Virchow:https://arxiv.org/abs/2309.07778UNI:https://arxiv.org/abs/2308.15474CONCH:https://arxiv.org/abs/2307.12914Mayo Clinic Atlas:https://arxiv.org/abs/2501.05409TITAN:https://arxiv.org/abs/2411.19666Dataset mentioned:The Cancer Genome Atlas (TCGA)https://portal.gdc.cancer.gov/Book mentioned:Digital Pathology 101: All You Need to Know to Start and Continue Your Digital Pathology Journeyhttps://digitalpathologyplace.com/Platform:Digital Pathology Placehttps://digitalpathologyplace.com/Support the showGet the "Digital Pathology 101" FREE E-book and join us!

June 17, 2026Episode 24141 min

241: AI-Powered Companion Diagnostics: The Future of Precision Medicine | Podcast with Doug Bowman, VP Precision Medicine at Indica Labs, Inc.

Send us Fan MailHow far can pathologists take visual biomarker scoring before human vision becomes the bottleneck?In this episode, I talk with Doug Bowman. PhD, VP Precision Medicine at Indica Labs, about what happens when companion diagnostics move from traditional visual scoring into the era of AI-powered image analysis. Doug comes from a biomedical and electrical engineering background, with experience in microscopy, digital image analysis, pharma workflows, and now precision medicine at Indica Labs. That combination makes him a great person to talk to about how image analysis actually fits into real companion diagnostic development.We start with a very practical question: what is a companion diagnostic, and why is it becoming so important in precision medicine? Doug explains that companion diagnostics are developed alongside therapeutics to help identify which patients are most likely to benefit from a specific treatment, especially in more complex therapies like antibody-drug conjugates (ADCs). We use HER2 as an example, and from there we get into the real challenge: once a biomarker cutoff matters clinically, visual estimation around that cutoff becomes much harder than many people want to admit.That is where this conversation gets especially useful for pathologists and digital pathology trailblazers. We talk about the limits of human vision, why low or ultra-low biomarker expression is difficult to score consistently, and how AI helps at multiple levels of the workflow: slide QC, tissue classification, cell segmentation, membrane and cytoplasmic measurement, and spatial analysis. Doug makes the case that AI is not only a convenience here. In some cases, it is the only realistic way to capture the kind of quantitative information modern therapies need.We also get into one of the more interesting examples from the episode: the Trop2 story, where a ratio of cytoplasmic to membrane expression appears to predict therapeutic efficacy better than looking at one compartment alone. That kind of compartment-level quantitation is exactly where computational pathology becomes more than a digital version of what the eye already does. It starts uncovering measurements and signatures the eye cannot reliably extract on its own.Another important part of the discussion is workflow and regulation. Doug walks through how AI-powered companion diagnostics are developed from preclinical work, to human feasibility studies, to RUO or clinical trial assays, and eventually toward analytical and clinical validation with regulatory engagement happening early. We also talk about the Indica Labs and Leica Biosystems partnership, and why end-to-end capability matters when you are trying to build something clinically deployable rather than just analytically interesting.What I liked about this conversation is that it stayed grounded. We did not talk about AI as magic. We talked about image analysis as a method, companion diagnostics as a workflow, and precision medicine as something that only works when the measurement is good enough to support real decisions.Episode Highlights00:00 – Why AI matters in slide QC, tissue classification, and cell-level analysis before you even get to the biomarker score.00:54 – Doug Bowman’s background in biomedical engineering, microscopy, and digital image analysis.05:16 – What a companion diagnostic actually is, and why it is critical for targeted therapies and ADCs.07:34 – Why visual biomarker scoring becomes unreliable around critical cutoffs, especially in low-expression cases.10:09 – How AI expands the workflow: slide QC, tissue classification, and precise cell segmentation.13:07 – Why pathologists remain central in AI workflows through validation, markup review, and model refinement.16:31 – The Trop2 example: when cytoplasmic-to-membrane ratio tells you more than one compartment alone.20:23 – The Indica Labs + Leica Biosystems partnership and why end-to-end workflow matters in companion diagnostics.22:53 – What the development journey looks like from early algorithm work to RUO, validation, and regulatory interaction.26:51 – Multiplexing, spatial analysis, and why more clinical value often comes with more deployment complexity.33:29 – Why image analysis literacy matters, and how shared language between pathologists and scientists becomes essential.40:13 – Where to learn more about Indica Labs and who to contact for collaboration.Resources mentionedIndica Labs Indica Labs contact – info@indicalab.comHALO software / HALO AI diagnostic image analysis – discussed in the context of companion diagnostic deployment and pharma services.Leica Biosystems GT450DX – referenced as an FDA-cleared slide scanner in the Indica-Leica partnership.Digital Pathology Association – mentioned as part of the broader educational ecosystem for digital pathology and image analysis.Digital Pathology Place / Digital Pathology Podcast – the platform hosting this conversation and related education around digital pathology and AI.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

June 3, 2026Episode 24033 min

240: Can AI Copilots Keep Up with Pathologists?

Send us Fan MailCan 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 Highlights01:20 – 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 mentionedDALPHIN 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.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

May 28, 2026Episode 23946 min

239: The Four Steps Pathology AI Demos Quietly Skip and Why They Matter

Send us Fan MailWhat happens when a pathology AI model misses the tissue before it even begins—and can better segmentation, education, and virtual staining close the gaps?In DigiPath Digest #47, I review four recent studies that expose both the promise and the weak points of AI-assisted digital pathology. We start with a step that sounds simple: detecting tissue on a whole slide image. Yet when that first step fails, the downstream algorithm may miss cancer entirely.From there, I look at a practical resource designed to improve communication between pathologists and computational scientists, a self-refining Segment Anything Model that reduces the burden of pixel-perfect nuclear annotations, and a virtual HER2 immunohistochemistry method generated from H&E images.The common lesson? Digital pathology AI is only as reliable as the workflow underneath it. Tissue processing, scanning, segmentation, annotation, model design, and clinical interpretation all matter. Every step needs validation.Discussion highlights00:00 – Welcome to DigiPath Digest #47 and an overview of the four papers02:31 – Why tissue detection is the foundation of pathology AI workflows05:01 – A large prostate pathology study using 33,823 whole slide images for tissue detection and 70,000 images for downstream Gleason grading06:28 – Classical thresholding missed tissue completely on 118 slides, compared with 24 slides using U-Net++ tissue detection07:51 – How artifacts and uneven slide color can disrupt classical detection methods09:59 – AI-based tissue detection reduced total failures by 79% but didn’t eliminate them10:39 – Why validating only the final algorithm output can hide upstream sources of error12:05 – Tissue detection choice produced clinically significant differences in final Gleason grade in 3.5% of malignant slides13:33 – The communication gap between pathology and computer science—and why it still slows progress15:29 – “Decoding Digital Histopathology” as an accessible guide for computational researchers entering pathology18:03 – Digital Pathology 101, my free companion resource for anyone starting or continuing a digital pathology journey20:18 – From collected tissue to glass slide, whole slide image, interpretation, and computational analysis25:08 – The role of TCGA and the growth of digital pathology datasets across clinical, veterinary, preclinical, and research settings27:14 – Why pixel-level nuclear annotation remains a bottleneck for deep learning30:39 – Replacing detailed nuclear outlines with simple point prompts31:23 – How the self-refining SAM framework uses sparse labels, contrastive learning, and a correction loop33:25 – Do we really need to keep annotating the same structures for every new model?37:49 – Generating virtual HER2 IHC from H&E with a score-aware, non-contrastive multitask model39:53 – Study results: 83.01% accuracy for virtual IHC alone and 97.85% accuracy when H&E and virtual IHC were combined40:56 – Where virtual IHC may fit as a complementary or triage tool—and why confirmatory testing still matters44:06 – My perspective on trust, interpretation, and the difference between predicting IHC and predicting molecular alterations46:17 – Final thoughts and an invitation to continue the discussionResources mentioned“Impact of Tissue Detection on Diagnostic Artificial Intelligence Algorithms in Prostate Digital Pathology” – Scientific Reports“Decoding Digital Histopathology: The Building Blocks for Computational Researchers” – PLOS Digital Health“Self-Refining Segment Anything Model for Nuclear Segmentation: A Contrastive Learning Approach to Label-Efficient Pathological Imaging” – Diagnostics“HER2 Score-Aware Virtual Immunohistochemistry via Non-Contrastive Multitask Translation” – DiagnosticsDigital Pathology 101: All You Need to Know to Start and Continue Your Digital Pathology JourneyEuropean Society of Digital and Integrative PathologyThe Cancer Genome Atlas (TCGA)If you work in pathology, computational science, image analysis, or AI development, this episode is a useful reminder to look beyond the headline performance metric. The errors that matter may begin much earlier in the workflow.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

May 19, 2026Episode 23819 min

238: How Do We Know AI Is Ready for Pathology

Send us Fan MailDo 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 Highlights00:00 – 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 pathologyResources MentionedDigital 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. Support the showGet the "Digital Pathology 101" FREE E-book and join us!