Send us a textIf our visual scoring is still based on gut feeling, how do we scale precision? In this week’s DigiPath Digest, I explored four new AI-focused papers that could reshape how we diagnose prostate, bladder, gastroesophageal, and endocrine cancers.From automated IHC scoring to predicting urethral recurrence post-cystectomy, these studies highlight the growing value—and responsibility—of integrating AI into our pathology workflows.And yes, I also reveal where to get my histology-inspired earrings 😉Episode Highlights[06:00] Muse Vet Platform launch + STP talk [11:00] Tools I use: Perplexity, RAG, ChatGPT, and AI citation traps [14:00] AI’s promise—and its pitfallsPaper 1: IHC Scoring in GEC (Caputo et al.)Manual PD-L1 and HER2 scoring is subjective. This study shows AI can standardize and improve accuracy using digital tools for GEC.[20:00] AI reduces visual bias [23:00] Potential to replace expensive assaysPaper 2: ASAP in Prostate BiopsiesPage Prostate AI matched final diagnoses 85% of the time—more than human reviewers.[24:00] ASAP = gray zone diagnosis [27:00] AI matched final calls more often than humansPaper 3: Recurrence Prediction Post-CystectomyChinese study developed a recurrence model using ML on clinical data. AUC: 0.86 (train), 0.77 (test).[30:00] Risk factors: CIS, bladder neck involvement [32:00] SHAP explained model insightsPaper 4: Reticulin Framework in Endocrine PathologyReticulin stains are cheap but powerful. This paper calls for AI to take notice.[36:00] Reticulin separates benign from malignant [40:00] Let’s train AI on these patterns📚 Resource from this EpisodeCaputo et al., Pathology Research & PracticePage Prostate study on ASAPML model predicting urethral recurrenceReticulin stains in endocrine tumor gradingAI is already enhancing diagnostic precision—we just need to guide its use responsibly. From special stains to advanced models, this episode covers where we're headed next.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

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