If 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 pitfalls
Paper 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 assays
Paper 2: ASAP in Prostate Biopsies
Page 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 humans
Paper 3: Recurrence Prediction Post-Cystectomy
Chinese 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 insights
Paper 4: Reticulin Framework in Endocrine Pathology
Reticulin 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 Episode
AI 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.