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Home > Digital Pathology Podcast > 181:Can AI Read Clinical Text, Tissue, and Costs Better Than We Can?


	Podcast:		Digital Pathology Podcast
	Episode:		181:Can AI Read Clinical Text, Tissue, and Costs Better Than We Can?
	Category:		Science & Medicine
	Duration:		00:34:59
	Publish Date:		2026-01-24 03:00:00
	Description:		Send us a text *What happens when artificial intelligence moves beyond images and begins interpreting clinical notes, kidney biopsies, multimodal cancer data, and even healthcare costs?* In this episode, I open the year by exploring four recent studies that show how AI is expanding across the full spectrum of medical data. From Large Language Models (LLM) reading unstructured clinical text to computational pathology supporting rare kidney disease diagnosis, multimodal cancer prediction, and cost-effectiveness modeling in oncology, this session connects innovation with real-world clinical impact. Across all discussions, one theme is clear: progress depends not just on performance, but on integration, validation, interpretability, and trust. HIGHLIGHTS: 00:00–05:30 \| Welcome & 2026 Outlook New year reflections, global community check-in, and upcoming Digital Pathology Place initiatives. 05:30–16:00 \| LLMs for Clinical Phenotyping How GPT-4 and NLP automate phenotyping from free-text EHR notes in Crohn’s disease, reducing manual chart review while matching expert performance. 16:00–23:30 \| AI Screening for Fabry Nephropathy A computational pathology pipeline identifies foamy podocytes on renal biopsies and introduces a quantitative Zebra score to support nephropathologists. 23:30–29:30 \| Is AI Cost-Effective in Oncology? A Markov model evaluates AI-based response prediction in locally advanced rectal cancer, highlighting when AI delivers value—and when it does not. 29:30–38:30 \| LLM-Guided Arbitration in Multimodal AI A multi-expert deep learning framework uses large language models to resolve disagreement between AI models, improving transparency and robustness. 38:30–44:30 \| Real-World AI & Cautionary Notes Ambient clinical scribing in practice, AI hallucinated citations, and why guardrails remain essential. KEY TAKEAWAYS • LLMs can extract meaningful clinical phenotypes from narrative notes at scale • AI can support rare disease diagnosis without replacing expert judgment • Economic value matters as much as technical performance • Explainability and arbitration are becoming critical in multimodal AI systems • Human oversight remains central to responsible adoption Resources & References Digital Pathology Place: https://www.digitalpathologyplace.com Digital Pathology 101 (free PDF, updates included) Automating clinical phenotyping using natural language processing Zebra bodies recognition by artificial intelligence (ZEBRA): a computational tool for Fabry nephropathy Cost-effectiveness analysis of artificial intelligence (AI) for response prediction of neoadjuvant radio(chemo)therapy in locally advanced rectal cancer (LARC) in the Netherlands A multi-expert deep learning framework with LLM-guided arbitration for multimodal histopathology prediction Support the show Get the "Digital Pathology 101" FREE E-book and join us!
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