PathAI

What’s next? Language unlocks potential capabilities beyond accuracy: 🔓 Open-vocabulary prediction: extend to new diagnoses with minimal tuning. 🔍 Natural language slide search: e.g. "slides showing interface dermatitis with basal vacuolization" 🩺 Further enrichment with case-specific clinical context Full blog post: pathai.com/blog/advancing… #DigitalPathology #ComputationalPathology #HealthcareAI #PLUTO4 #MultimodalAI

Looking deeper into the the shared embedding space, it has clinically meaningful structure: 🟢 Subtypes cluster together 🟤 Inflammatory dermatoses separate cleanly from melanocytic lesions Slide embeddings mirror text embedding organization, showing alignment across modalities, not just within them.

🔬 Moving Beyond Pixels: Advancing Multimodal Pathology Pathology AI has long relied on images alone. What happens when you add language? We combined PLUTO-4 vision embeddings with rich histological descriptions to build a joint vision-language space for disease classification, and the results are promising. 🧵 #AI #MachineLearning #Pathology #MultimodalAI #FoundationModels

These results highlight how our PLUTO-4 foundation models enhance PathAI’s AI-pathology products across digital diagnostics and translational research. We’re excited for the new capabilities PLUTO-4 will unlock for our partners and the community! 📄 Learn more in our technical report: 👉 arxiv.org/abs/2511.02826 #AI #Pathology #HealthcareAI #FoundationModels #PLUTO4

Beyond public benchmarks, PLUTO-4 shows real-world impact — 🩺 ~10 % improvement across multiple PathAI products, with strong gains in dermatopathology specimen classification. These advances bring us closer to robust, generalizable FMs for pathology applications. #Dermatology #HealthcareAI

🚀 Excited to share PLUTO-4, our new state-of-the-art foundation models for pathology! 🔬 We’re seeing SoTA performance across multiple public benchmarks (EVA and HEST) — surpassing other leading pathology foundation models. (1/6) #AI #MachineLearning #Pathology #FoundationModels #HealthcareAI

The opportunity to standardize the way we construct data sets is important...If we try to build a data set for every use case, we can set ourselves up to fail. We don't want to build a large reference data set that doesn't get used - @balasubramaniac from @Path_AI #FriendDx

🔍 Interpretable concepts found using SAE - SAE trained on PLUTO embeddings disentangled polysemantic features. Single dimensions captured distinct concepts: ✅ Cell types (e.g., cancer cells, red blood cells) ✅Geometric features (e.g. edge of tissue) ✅ Artifacts (surgical ink)

Monosemantic representations - Single SAE dimensions correlate with counts of single cell types. For example, SAE-1736 represents plasma cell abundance exclusively - The findings generalized to: ✅ Out-of-domain datasets (CPTAC) ✅ Different stains (H&E, IHC) ✅ Various scanners

PathAI #MachineLearning engineers have recently published new #AI findings for mechanistic interpretability of PLUTO, a pathology #foundationmodel. Using sparse autoencoders (SAEs), we uncovered biologically meaningful and interpretable features. 🧵 arxiv.org/html/2407.1078…

🔬 Impact This study shows the promise & potantial of SAEs in explaining foundation model behavior for medical imaging. Interpretable features unlock: - Potential for clinical AI 🏥 - New biological insights 🧪 🔗 Read the full work: bit.ly/4gl20xZ #AI #Pathology

Feature evolution across layers - SAEs trained on PLUTO’s intermediate layers revealed: Early layers → Low-level color/texture features 🎨 Later layers → Pathology-relevant biological features 🔬 (e.g., monosemantic plasma cell dimension).