Digital Pathology Podcast

Hosted by Aleksandra Zuraw, DVM, PhD

Technology Science Health FitnessInterviews guests

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252

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Oct 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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October 18, 2026Episode 25214 min

252: Digital Pathology Trends 2026: Agilent Buys Proscia | PathVisions

Send us Fan Mail What are the biggest digital pathology trends of 2026, and what does Agilent buying Proscia tell us? It's the clearest sign yet that the field is moving from shiny algorithms to full, connected workflows. In this live recap from Pathology Visions 2026 in San Diego, Dr. Aleks Zuraw (DVM, PhD, DACVP), board-certified veterinary pathologist and host of the Digital Pathology Podcast, sits down with Imogen Fitt of Signify Research to break down the biggest news of the week and the trends behind it. Imogen tracks the digital pathology market for a living. Aleks brings the pathologist's view. Together they cover the Agilent and Proscia deal, why software is finally being built around pathologist ergonomics, how scanner differences can change AI results, and whether every AI tool needs FDA clearance or can live as a lab-developed test (LDT). Key takeaways - Agilent's acquisition of Proscia is the peak of a partnership trend: everyone wants to be compatible with everyone. - This year's software news centers on ergonomics, voice dictation and voice AI, not just new algorithms. - PathVisions posters compared scanners and showed that image differences between models can change AI output. Purpose and intended use have to be defined within a whole system. - "Good enough" can be the right specification when AI is a safety net that flags cases for a human. - Not everything needs to be a medical device. The LDT pathway lets single labs start, gather data, and decide later whether to pursue clearance. - Pay-per-scan models and implementation work, not hype, are what will get digital pathology to more labs. Episode Highlights 00:36 - Agilent acquires Proscia - The biggest digital pathology news heading into PathVisions. 01:10 - Aperture and Concentriq - AI that finds clinical trial patients and an AI-enabled image management system. 01:43 - Ergonomics and voice AI - Why nobody likes pop-up windows, and what's replacing them. 02:16 - From staining to sign-out - Aleks recalls first seeing Agilent's full workflow at USCAP. 03:39 Scanners change AI output - What this year's posters showed, and why it surprised a pathologist. 05:09 - Good enough vs perfect - Imogen plays devil's advocate on accuracy targets. 07:15 - The pixel pathway - How locked systems ended up driving interoperability. 08:02 - FDA clearance or LDT? - Does every pathology AI tool need to be a medical device? 11:03 - Implementation over hype - Why the "boring" topics are the real progress. 12:03 - Pay-per-scan - Getting digital pathology to labs that can't buy a scanner upfront. Resources Mentioned - Signify Research: https://www.signifyresearch.net/ - Pathology Visions 2026: https://digitalpathologyassociation.org/pathology-visions-conference - Free Digital Pathology 101 book: https://www.aleksandrazuraw.com/book Connect with Imogen Fitt on LinkedIn: https://www.linkedin.com/in/imogen-fitt-194566127/ Enjoyed this episode? Share it with one colleague who's still deciding whether to go digital. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

October 15, 2026Episode 25126 min

251: Pathology Foundation Models: PathBench, Scanner Shift, and the HER2 Slide Swing

Send us Fan Mail What if changing the scanner or choosing another slide from the same tumor changes your pathology AI results? In DigiPath Digest #52, I review three studies on pathology foundation models, scanner shift, and slide selection to ask how much of “model performance” comes from the model itself. We look at a benchmark comparing 32 foundation models across 41 tasks, a study testing 10 models across scanners and centers, and a breast cancer study examining how predictions change when different slides represent the same patient. The findings challenge familiar assumptions about bigger models, benchmark rankings, and the idea that one slide can represent a heterogeneous tumor. Combining information from multiple slides improved performance in the breast cancer study, without changing the model architecture. My takeaway: evaluate the model, the data, and the workflow together. I share three practical questions to ask before accepting an AI benchmark score. Highlights with Timestamps 00:42 — PathVisions updates and plans for conference activities. 05:09 — Trailblazer podcast interviews: sharing real implementation experience. 08:02 — How much of foundation model performance comes from the model? 13:16 — The 32-model benchmark and closely grouped leading scores. 16:06 — Why bigger models do not consistently perform better on unfamiliar data. 18:58 — Scanner shift and testing models on scanners held out during training. 22:18 — Why AUROC alone can miss important performance losses. 24:55 — Use-case-specific validation and the “pixel pathway” from scanning to diagnosis. 28:37 — Slide selection, tumor heterogeneity, and HER2 prediction. 33:46 — Combining slides from the same patient: performance gains and computational cost. 35:31 — Three questions to ask before trusting a benchmark. 37:27 — Audience Q&A on available datasets and checking AI-generated answers. 40:39 — Plans for the Digital Pathology Club and more accessible expert guidance. Resource from this episode Research papers: A benchmark study of vision and pathology foundation models for computational pathology Domain Generalization of Histopathology Foundation Models in Multicenter, Multi-Scanner Cohorts: A Comparative Benchmark Slide Selection Introduces Sampling-Induced Prediction Uncertainty in Digital Pathology AI: Evidence from Multi-Slide Breast Cancer Cohorts Data resource mentioned: The Cancer Genome Atlas (TCGA) through the Genomic Data Commons (GDC) portal Support the show Get the "Digital Pathology 101" FREE E-book and join us!

October 8, 2026Episode 25021 min

250: AI for Scientific Writing: 4 Rules from a Pathologist

Send us Fan Mail Can you use AI to write scientific papers without fabricated references, journal trouble or learning to code? I recorded this episode at the airport on my way to ESTP 2026 in Kraków. It's my practice run of the talk I gave there. When deep learning came into pathology, people asked me if I was going to learn to code. I decided no. Large language models kind of proved me right. In this episode I walk you through how I use LLMs for writing papers, responding to co-authors, building slides and even running code, and the four rules that keep it safe: Domain expertise is the prompt Supply the sources Encode corrections The subject matter expert verifies In this episode: What the LLM does vs. what the pathologist does, task by task What Toxicologic Pathology, Veterinary Pathology and Elsevier journals require when you disclose AI use Pathology LLM tools already working on tox study reports, image analysis and whole slide images The reference I didn't check that almost cost me a publication Why an LLM will happily justify switching patients from Tylenol to acetaminophen Automation bias, model bias, and how to start with the tools your institution has approved Going to PathVisions 2026 in San Diego, Oct 16-18? Find me at the Hamamatsu booth #201. I'm also recording 8 short podcast interviews on site, Saturday and Sunday. Share your digital pathology story, grab a slot: https://calendar.google.com/calendar/appointments/schedules/AcZssZ0yjFhaL0kGJ7bZ18oITwyw-qY2yxvcDEyfqpj3LBulrRdiO4fRUkgM0Avlj_oJkIaw-0ZjhaC9 Links Free book, Digital Pathology 101: https://www.aleksandrazuraw.com/book Related episode: How to teach AI to healthcare professionals, with Dr. Candice Chu: Watch the video version on YouTube: https://youtu.be/eY7UywT-zRM?si=qK88eECJqoA-DqKF References Zuraw & Aeffner, Vet Pathol 2022. doi:10.1177/03009858211040484 Turner et al., Toxicol Pathol 2021. doi:10.1177/0192623321990375 Thirunavukarasu et al., Nat Med 2023. doi:10.1038/s41591-023-02448-8 Lu et al., J Transl Med 2026. doi:10.1186/s12967-026-08829-0 Doktorova et al., Arch Toxicol 2026. doi:10.1007/s00204-026-04414-y Trost et al., Nat Med 2026. doi:10.1038/s41591-026-04357-y Chen Y et al., Nat Cancer 2026. doi:10.1038/s43018-026-01220-4 Chu, Front Vet Sci 2024. doi:10.3389/fvets.2024.1395934 Jain et al., Cureus 2025. doi:10.7759/cureus.81618 Kurland et al., Neurosurgery 2025. doi:10.1227/neu.0000000000003354 Painter et al., JAMIA Open 2025. doi:10.1093/jamiaopen/ooaf003 Chen S et al., NPJ Digit Med 2025. doi:10.1038/s41746-025-02008-z Dratsch et al., Radiology 2023. doi:10.1148/radiol.222176 Zack et al., Lancet Digit Health 2024. doi:10.1016/S2589-7500(23)00225-X FDA Warning Letter 722591, Apr 2026 EMA/FDA, Guiding principles of good AI practice in drug development, Jan 2026 Huang & Chu, Front Vet Sci 2026. doi:10.3389/fvets.2026.1801756 Support the show Get the "Digital Pathology 101" FREE E-book and join us!

August 31, 2026Episode 24930 min

249: Cytopathology AI: Crowded-Cell Gaps and LLM Guardrails

Send us Fan Mail What happens when AI models that perform almost perfectly on scattered cervical cells become less reliable than a coin flip on crowded cell groups? In DigiPath Digest #51, I examine what this performance gap tells us about artificial intelligence in cytopathology. The first paper evaluated six convolutional neural network models trained to distinguish benign from high-grade lesions using scattered cervical cytology cells. The models achieved AUCs ranging from 0.950 to 0.996 on the original images. When the same models were applied to hyperchromatic crowded cell groups without retraining or threshold recalibration, their AUCs fell to 0.385-0.683. Different architectures also failed differently. Some overcalled benign clusters, while others missed high-grade clusters. This isn’t simply a technical problem. It illustrates a practical rule for pathology AI: a model should only be trusted for the morphology, specimen type, imaging system, and intended use on which it has been directly validated. I also review the current evidence for large language models in cytopathology. Potential applications include structured reporting, diagnostic support, quality control, education, research, and workflow integration. Some early results appear promising, but each comes with important limitations. One structured-reporting application achieved 99.4% accuracy at a single institution. A diagnostic-support model included the correct answer among its top 10 differentials in 59.1% of general medicine cases. A hybrid quality-control system flagged 84% of errors associated with amended reports, but its false-positive rate wasn’t reported. Most importantly, the review found no language model specifically trained and clinically validated on cytopathology reports. The takeaway is straightforward: we’re still working with narrow AI. Strong performance in one setting doesn’t guarantee performance when the cells, preparation, scanner, institution, or clinical task changes. Low-risk applications may offer the most practical starting point. Text extraction, completeness checks, report consistency review, and quality-control flagging could reduce repetitive work without asking an unvalidated model to make the final diagnosis. Highlights with timestamps 00:00 - Welcome to DigiPath Digest #51 and the new lunch-and-learn time 01:40 - How two image models performed worse than a coin flip on cell clusters 02:29 - Image models, language models, and vision-language models 04:40 - Why AI adoption in cytopathology remains low 05:25 - Scattered single cells versus hyperchromatic crowded cell groups 08:14 - AUCs fall from 0.950-0.996 to 0.385-0.683 10:02 - How ResNet-50 and GoogLeNet failed differently 11:04 - What the attention maps revealed 13:22 - The intended-use lesson for pathology AI 17:02 - Current applications of large language models in cytopathology 18:39 - Structured reporting and the 99.4% accuracy result 19:32 - Diagnostic support, AMIE, and the top-10 limitation 21:09 - Quality-control applications and the missing false-positive rate 23:30 - Why cytopathology still needs domain-specific language models 24:54 - Retrieval-augmented generation, education, and research support 27:07 - Two AI families, one requirement: direct validation 28:35 - Context of use and intended-use validation 29:12 - Human-reviewed training data and destructive book scanning 32:02 - FDA and European approaches to evolving AI models 35:00 - Protecting patient and practitioner well-being 36:21 - Cytopathology-specific benchmarks and shared test sets 38:16 - Why low-risk AI applications should come first 39:50 - Cytopathology’s direct-to-digital advantage 40:39 - Digital Pathology 101 and Pathology Visions Resources from this episode Watch DigiPath Digest #51 Diagnostic performance of AI models trained on scattered single-cell images Leveraging large language models to enhance cytopathology FDA guidance on AI and context of use FDA guidance on predetermined change control plans Google Research: AMIE diagnostic medical AI EU In Vitro Diagnostic Medical Devices Regulation EU Artificial Intelligence Act Digital Pathology 101 Pathology Visions 2026 Support the show Get the "Digital Pathology 101" FREE E-book and join us!

August 20, 2026Episode 24853 min

248: Are Foundation Models Really Better for Digital Pathology? Podcast with Panu Kauppila

Send us Fan Mail What good is a powerful foundation model if it slows the pathologist down, can’t explain its result, or doesn’t fit the clinical workflow? Foundation models are gaining attention across digital pathology. But they’re not finished clinical tools by themselves. In this episode of the Digital Pathology Podcast, I speak with Panu Kauppila, Chief Product Officer at Aiforia , about what foundation models are, how they differ from traditional convolutional neural networks, and what it takes to make them useful for pathologists. Panu describes a foundation model as a large, context-aware building block. To perform a specific pathology task - such as grading, segmentation, or mitotic counting - it must be combined with an adapter, a task-specific head, curated annotations, a usable interface, and integration with the laboratory workflow. We also discuss one of the biggest practical constraints: speed. A pathologist shouldn’t have to click a button and wait for an analysis. Panu explains why AI should run automatically in the background so the results are already available when the case reaches the pathologist’s worklist. The conversation also examines the tradeoff between model size, computational cost, and clinical performance. Larger models may improve robustness and generalizability, but they can also require more processing power. For clinical applications, Aiforia focuses on smaller and medium-sized foundation models that provide the necessary quality without making the workflow slower or unnecessarily expensive. Annotated data remains central. The foundation model supplies the underlying image understanding, while the task-specific head and controlled annotations determine how the model performs on a particular pathology problem. This structure also raises important questions about bias, data provenance, ownership, regulatory documentation, and explainability. Finally, we look at multimodal AI models that combine pathology images with reports, genomic data, molecular information, and clinical outcomes. These tools could support more interactive, predictive, and prognostic applications - but only if they’re introduced through secure, controlled workflows with clear audit trails. Episode Highlights 00:00 — Where does bias enter a foundation model workflow? Panu distinguishes the underlying foundation model from the annotated dataset used to build the task-specific application. 00:27 — Meet Panu Kauppila An introduction to Aiforia’s Chief Product Officer and the episode’s focus on foundation models in digital pathology. 01:05 — From radiology AI to digital pathology Panu describes his background in medical device development, radiology, oncology solutions, and clinical AI implementation. 06:29 — Foundation models versus convolutional neural networks What makes foundation models more context-aware, robust, and generalizable across image datasets. 07:07 — Image-only and multimodal foundation models Why these two categories offer different capabilities and potential clinical uses. 07:50 — A foundation model is a platform, not a finished solution The underlying model may understand image features, but it still needs a task, interface, and clinical workflow. 08:34 — Foundation model, adapter, and task-specific head How these components work together to create an application for grading, mitotic counting, or another pathology task. 09:57 — The cost of larger models Why increased robustness must be balanced against computational demands, inference speed, and affordability. 11:12 — Pathologists won’t wait for AI Why even short delays can interrupt the clinical workflow. 11:46 — Running AI in the background A workflow in which slides are scanned, analyzed automatically, and added to the worklist with results ready for review. 12:16 — Combining foundation models with curated annotations How smaller task-specific datasets and adapter technology can produce practical pathology models. 15:24 — Generalizability across scanners, laboratories, and populations How foundation models may make adaptation to new domains more manageable. 16:36 — Two datasets, two sources of potential bias The regulatory questions created by an underlying foundation model and a separate controlled annotated dataset. 20:46 — Making foundation models accessible Why a platform and user interface are necessary for pathologists and researchers who don’t work directly with code. 21:57 — Testing foundation models in Aiforia Create How researchers can compare a CNN with supported foundation models in the same no-code environment. 25:45 — How foundation models are selected Quality, licensing, model size, annotated data, and the requirements of the intended use. 26:57 — Training cost versus inference cost Why a more expensive training iteration may still reduce total development costs if fewer iterations are needed. 32:23 — Pathologists are visual reviewers The importance of segmentation quality and showing exactly what the model identified. 36:26 — The potential of multimodal AI Combining pathology images with text, genomic information, molecular data, and clinical outcomes. 37:35 — Keeping multimodal AI inside a controlled environment Privacy, security, regulatory oversight, and the risks of moving clinical information into consumer AI tools. 43:24 — Explainability in clinical pathology AI Using semantic segmentation, object detection, instance segmentation, and annotated ground truth to show how results were calculated. 47:43 — Moving toward predictive and prognostic models How established digital workflows could allow pathologists to contribute more information about likely outcomes. 49:45 — Research and clinical collaboration with Aiforia How interested researchers and laboratories can connect through the Aiforia website. Resources Mentioned Aiforia Aiforia Create no-code model-development environment PathChat Listen to the full discussion to understand what foundation models can add to digital pathology—and what still has to happen before they become practical, trusted clinical tools. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

August 17, 2026Episode 24728 min

247: Screening Efficiency Over Experience: Rethinking Cytology Expertise

Send us Fan Mail Does 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 Highlights 00:00 – Welcome to DigiPath Digest #50 and introduction to the paper 04:10 – Why the traditional definition of professional expertise is changing 07:02 – Moving from exhaustive searching to verification in AI-assisted workflows 09:04 – How eye tracking was used with 100 board-certified professionals 10:25 – Years of experience versus diagnostic accuracy 13:13 – Experience-based caution and attention to sample information 15:16 – Low-power field efficiency as a predictor of high accuracy 17:05 – Practical low-power field demonstration using a whole slide image 20:15 – Searching versus detecting and the mental map of normal morphology 24:04 – Comparing high- and low-performer visual scan paths 25:27 – Cognitive filtering: knowing what not to examine 27:35 – Can visual efficiency be taught in three months? 29:55 – How AI may shift the human role from searcher to verifier 30:38 – Study limitations: static images versus dynamic whole slide imaging 32:37 – Could gaze efficiency influence future competency assessment? 33:44 – Digital pathology learning resources and closing thoughts Resources Mentioned Abstract and paper: Screening Efficiency Over Experience: Rapid Target Detection in Low-Power Field as a Modifiable Cognitive Biomarker for Diagnostic Accuracy in Digital Cytology Listen 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 show Get the "Digital Pathology 101" FREE E-book and join us!

August 13, 2026Episode 2461 hr 7 min

246: Computational Pathology Is Changing Companion Diagnostics

Send us Fan Mail Can 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 pathologist Saleh Miri, PhD, Director of Digital Pathology AI Algorithms Purvi Gaglani, Regulatory Affairs Lead for Digital Pathology We 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 Highlights 00:00 — Pathologists remain central to computational CDx Why computational tools provide more precise measurements without replacing pathology expertise. 01:09 — Why companion diagnostics are changing Visual IHC scoring helped launch precision oncology, but the model is approaching its limits. 04:53 — The current companion diagnostic landscape How IHC, next-generation sequencing, liquid biopsy, and visual biomarker scoring are used today. 07:01 — The mathematical burden placed on the human eye Why manually assessing tens of thousands of tumor cells requires pathologists to estimate rather than calculate. 08:39 — The borderline patient dilemma A digital tool can distinguish measurements such as 74% and 76%, while that difference is difficult to reproduce visually. 09:27 — Why spatial context matters Computational pathology can measure biomarker heterogeneity, clustering, and relationships between tumor and immune cells. 12:17 — Where manual scoring reaches its limits Interobserver variability, intraobserver variability, fatigue, staining interpretation, and heterogeneous tumors. 18:29 — Moving from judgment calls to quantified measurements Why the next stage of precision oncology requires information beyond human visual perception. 19:14 — Computer-assisted scoring versus computational CDx The important distinction between helping a pathologist calculate an existing score and generating a new algorithmic measurement. 23:22 — Computational pathology and decentralized workflows How digital images can support remote review, access to expertise, and second opinions. 27:48 — Why therapies require higher-resolution biomarkers Modern targeted treatments may respond to biological differences that categorical scoring can’t capture. 32:09 — TROP2 in advanced non-small cell lung cancer The episode’s example of a biomarker requiring computational measurement. 33:26 — Understanding the normalized membrane ratio How the algorithm measures membrane expression relative to total protein expression at the individual-cell level. 35:46 — Working with regulators on a new diagnostic model Purvi discusses global health authority engagement and the FDA Breakthrough Device Designation. 38:33 — The computational CDx as a system of systems Why staining, scanning, image management, algorithms, displays, and reporting must be evaluated together. 40:11 — Changes to validated workflow components How using a different scanner, monitor, or other component could fall outside the defined device configuration. 43:30 — Why computational pathology is becoming necessary Continuous measurements can reveal biomarker-treatment relationships that may remain hidden within categorical scores. 49:12 — The pathologist’s role in the workflow Reviewing sample, staining, scan, image, algorithmic analysis, and the final biomarker result. 53:39 — Digital second opinions How image management systems can simplify collaboration without physically transporting glass slides. 56:43 — What laboratories need to prepare Validated infrastructure, cybersecurity, preanalytical control, training, and digital pathology literacy. 58:49 — Learning to interpret computational results The shift from visually estimated categories to continuous, quantitative biomarker measurements. Resources Mentioned Full discussion on YouTube: https://youtu.be/oKW1xC6TTZg Listen 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 show Get the "Digital Pathology 101" FREE E-book and join us!

July 29, 2026Episode 24545 min

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

Send us Fan Mail Is your digital pathology rollout moving so slowly that it’s creating a fragmented workflow instead of transforming the department? In this episode of the Digital Pathology Podcast, I speak with Dr. Syed Hoda, Director of Digital Pathology at NYU, about why gradual implementation may no longer be the best approach to digital pathology adoption. Dr. Hoda explains how NYU used an intensive nine-month planning period to prepare for a department-wide transition. The process involved pathology, IT, project managers, vendors, hospital leadership, and approximately 40–50 people participating in regular planning calls. This wasn’t simply a scanner installation. The team mapped workflows, configured Epic Beaker, redesigned laboratory spaces, tested integrations, planned training, and addressed the practical concerns of nearly 100 pathologists. We also discuss why scanner specifications may matter less than integration, vendor support, training, and system performance. For Dr. Hoda, digital pathology had to work as smoothly as glass microscopy. Speed was non-negotiable. Change management played an equally important role. Through open discussions, town halls, and the ADKAR framework, the team addressed concerns ranging from ergonomics to the loss of collaborative microscope sessions. The result? Every pathologist adopted the digital workflow, no one left the department because of the transition, and approximately 60–65 pathologists now work remotely using equipment that matches their office setup. Finally, we examine the next step: artificial intelligence in pathology. Dr. Hoda explains why NYU focused on building a reliable digital foundation before introducing AI. He also raises important questions about validation, transparency, responsibility, regulatory clearance, and the need for greater pathologist involvement in AI development. Episode Highlights 00:00 — Are we repeating the same mistakes with pathology AI? Dr. Hoda compares the current excitement around AI with the early promises made about digital pathology 15 years ago. 01:04 — Meet Dr. Syed Hoda His clinical pathology background and path to becoming NYU’s Director of Digital Pathology. 03:16 — Why going slowly can hold departments back How partial adoption creates fragmented workflows, inconsistent training, and prolonged implementation. 06:25 — Leadership support for rapid adoption Why institutional commitment, resources, and an ambitious timeline made the project possible. 10:13 — Nine months of detailed planning Workflow mapping, laboratory changes, system configuration, vendor selection, testing, and validation. 11:48 — The role of professional project management Why pathologists shouldn’t be expected to coordinate every part of a complex digital transformation. 14:29 — Why the scanner isn’t the most important decision Image quality matters, but integration, service, training, and workflow fit may matter more. 17:42 — People matter more than machines How vendor relationships and departmental engagement supported adoption. 19:19 — Setting clear expectations across the department NYU communicated that every pathologist would move to digital sign-out within a defined period. 20:49 — Change management is a structured process How the ADKAR framework guided communication, education, adoption, and reinforcement. 25:07 — Addressing practical and personal concerns From mouse ergonomics to preserving collaborative case review between pathologists. 27:19 — Why NYU didn’t introduce AI first Dr. Hoda explains why pathologists needed to become comfortable with the digital platform before adding new AI tools. 29:26 — Digital pathology and remote sign-out Approximately 60–65 pathologists now work remotely with equipment matching their office setup. 30:28 — Why speed is non-negotiable Even a small delay or repeated pixelation can quickly undermine confidence in a digital workflow. 33:25 — A cautious approach to pathology AI Concerns about premature adoption, self-validation, limited regulatory clearance, and lack of pathologist involvement. 37:27 — Scientific validation, transparency, and responsibility What happens when the AI result and the pathologist’s interpretation don’t agree? 40:41 — Where AI could meaningfully augment pathology Quantifying microenvironments, feature combinations, ratios, and findings that are difficult to assess visually. Resources Mentioned ADKAR change management framework Digital Pathology Association Executive War College FDA list of AI-powered medical devices A radiology mock-trial paper examining responsibility when clinicians use AI: Examining perceptions of liability about AI in radiology (MedRxiv) Why AI cannot do good science without humans ( Nature Editorial) A previous Digital Pathology Podcast discussion about AI-supported colorectal cancer feature analysis (How to use deep learning image analysis for colon cancer with Rish Pai) Listen to the full conversation for a practical look at digital pathology planning, change management, remote sign-out, scanner integration, and responsible AI adoption. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

July 22, 2026Episode 2441 hr 37 min

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

Send us Fan Mail If 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 Highlights 00:00 — 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 Podcast MIT 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 show Get the "Digital Pathology 101" FREE E-book and join us!

July 2, 2026Episode 24344 min

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

Send us Fan Mail What 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 Highlights 00:00 – Why AI tools are just tools, and why trying them matters even if you later decide not to keep using them 00:33 – Who Candice Chu is and why her work on AI literacy in veterinary medicine is worth paying attention to 02:33 – Why going back to Texas A&M changed the scale of Candice’s AI research and teaching 07:53 – How the AI course was designed as a low-stakes elective first, and why that helped student engagement 11:16 – Where veterinary AI education stands now, and what professional organizations like ACVP are doing 13:08 – Why AI adoption in veterinary medicine is still slow, and what skepticism usually sounds like in practice 15:19 – Real examples of how Candice uses LLMs and computer vision in pathology, medical records, and research 19:58 – What is actually inside the 15-week AI literacy curriculum, from fundamentals to final projects 24:16 – Why ethics and legal responsibility are not optional in AI education 31:35 – Why no-code tools and vibe coding are entering the curriculum already 38:50 – The AI tools Candice is testing in her own workflow, including Claude, Codex, and Perplexity Resources mentioned Candice Chu’s AI literacy curriculum framework paper in Frontiers in Veterinary Science Candice’s earlier work on ChatGPT in veterinary medicine Texas A&M and the institutional setting where Candice is building AI research and teaching Mr. Don Riddick and the AVMA AI working group, mentioned in the ethics and legal context Claude, Codex, and Perplexity as AI tools Candice is actively testing Digital Pathology 101 , mentioned in the conversation as a teaching resource Candice’s online educational work on Instagram . Support the show Get the "Digital Pathology 101" FREE E-book and join us!