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Digital Pathology Podcast

Digital Pathology Podcast

Written by: Aleksandra Zuraw DVM PhD
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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.© 2026 Digital Pathology Podcast Hygiene & Healthy Living Nature & Ecology Physical Illness & Disease Science
Episodes
  • 249: Cytopathology AI: Crowded-Cell Gaps and LLM Guardrails

    249: Cytopathology AI: Crowded-Cell Gaps and LLM Guardrails
    Aug 31 2026
    Send us Fan MailWhat 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 timestamps00:00 - Welcome to DigiPath Digest #51 and the new lunch-and-learn time01:40 - How two image models performed worse than a coin flip on cell clusters02:29 - Image models, language models, and vision-language models04:40 - Why AI adoption in cytopathology remains low05:25 - Scattered single cells versus hyperchromatic crowded cell groups08:14 - AUCs fall from 0.950-0.996 to 0.385-0.68310:02 - How ResNet-50 and GoogLeNet failed differently11:04 - What the attention maps revealed13:22 - The intended-use lesson for pathology AI17:02 - Current applications of large language models in cytopathology18:39 - Structured reporting and the 99.4% accuracy result19:32 - Diagnostic support, AMIE, and the top-10 limitation21:09 - Quality-control applications and the missing false-positive rate23:30 - Why cytopathology still needs domain-specific language models24:54 - Retrieval-augmented generation, education, and research support27:07 - Two AI families, one requirement: direct validation28:35 - Context of use and intended-use validation29:12 - Human-reviewed training data and destructive book scanning32:02 - FDA and European approaches to evolving AI models35:00 - Protecting patient and practitioner well-being36:21 - Cytopathology-specific benchmarks and shared test sets38:16 - Why low-risk AI applications should come first39:50 - Cytopathology’s direct-to-digital advantage40:39 - Digital Pathology 101 and Pathology VisionsResources from this episodeWatch DigiPath Digest #51Diagnostic performance of AI models trained on scattered single-cell imagesLeveraging large language models to enhance cytopathologyFDA guidance on AI and context of useFDA guidance on predetermined change control plansGoogle Research: AMIE diagnostic medical AIEU In Vitro Diagnostic Medical Devices RegulationEU Artificial Intelligence ActDigital Pathology 101Pathology Visions 2026 Support the showGet the "Digital Pathology 101" FREE E-book and join us!
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    31 mins
  • 248: Are Foundation Models Really Better for Digital Pathology? Podcast with Panu Kauppila

    248: Are Foundation Models Really Better for Digital Pathology? Podcast with Panu Kauppila
    Aug 20 2026
    Send us Fan MailWhat 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 Highlights00:00 — 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 KauppilaAn 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 pathologyPanu describes his background in medical device development, radiology, oncology solutions, and clinical AI implementation.06:29 — Foundation models versus convolutional neural networksWhat makes foundation models more context-aware, robust, and generalizable across image datasets.07:07 — Image-only and multimodal foundation modelsWhy these two categories offer different capabilities and potential clinical uses.07:50 — A foundation model is a platform, not a finished solutionThe underlying model may understand image features, but it still needs a task, interface, and clinical workflow.08:34 — Foundation model, adapter, and task-specific headHow these components work together to create an application for grading, mitotic counting, or another pathology task.09:57 — The cost of larger modelsWhy increased robustness must be balanced against computational demands, inference speed, and affordability.11:12 — Pathologists won’t wait for AIWhy even short delays can interrupt the clinical workflow.11:46 — Running AI in the backgroundA 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 annotationsHow smaller task-specific datasets and adapter technology can produce practical pathology models.15:24 — Generalizability across scanners, laboratories, and populationsHow foundation models may make adaptation to new domains more manageable.16:36 — Two datasets, two sources of potential biasThe regulatory questions created by an underlying foundation model and a separate controlled annotated dataset.20:46 — Making foundation models accessibleWhy 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 CreateHow researchers can compare a CNN with supported foundation models in the same no-code environment.25:45 — How foundation models are selectedQuality, licensing, model size, annotated data, and the requirements of the intended use.26:57 — Training cost versus inference costWhy a more expensive training iteration may still reduce total development costs if fewer iterations are needed.32:23 — Pathologists are visual reviewersThe importance of segmentation quality and showing exactly what the model ...
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    53 mins
  • 247: Screening Efficiency Over Experience: Rethinking Cytology Expertise

    247: Screening Efficiency Over Experience: Rethinking Cytology Expertise
    Aug 17 2026

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    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.

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    Get the "Digital Pathology 101" FREE E-book and join us!
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    29 mins
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