Connecting Visual and Textual Data: Our Vision for Multimodal AI
Multimodal AI
Connecting Images & Text in Pathology
The future of computational pathology lies in seamlessly combining image analysis with text data. While most platforms handle images OR text, the real breakthrough comes from systems that integrate both. Here's our vision for building multimodal AI capabilities into NanoView.
Why Multimodal Matters
Pathologists don't work in isolation. They combine visual observations from slides with textual information from clinical histories, previous reports, and research literature. Yet most digital pathology platforms treat images and text as separate systems.
Major pharmaceutical companies are already investing heavily in multimodal AI systems that combine image analysis and NLP to accelerate drug development. The same approach can transform clinical and educational pathology workflows.
The Multimodal Workflow
Imagine a pathologist working on a case with our multimodal platform:
Step 1: Image Analysis
The pathologist opens a slide. AI immediately highlights suspicious regions, counts nuclei, and suggests quantitative measurements. These visual insights are stored with precise coordinates and metadata.
Step 2: Text Integration
As the pathologist views the slide, they can see the patient's clinical history, previous pathology reports, and relevant research papers—all linked to the current case. NLP extracts key information and presents it contextually.
Step 3: Dictation & Structuring
The pathologist dictates observations: "I see atypical cells with high mitotic activity, consistent with grade 3 invasive ductal carcinoma." NLP structures this into a report template, automatically linking the text to the specific regions on the slide.
Step 4: Multimodal Search
The pathologist asks: "Show me similar cases." The system searches using both image features (morphology, staining patterns) and text (diagnosis, clinical history), returning results ranked by multimodal similarity.
Step 5: AI-Assisted Diagnosis
The system suggests diagnoses based on both visual patterns and textual context. It might say: "Based on the morphology (high mitotic activity, atypical cells) and clinical history (age 65, mammography finding), this is consistent with grade 3 IDC. Similar cases had 85% concordance with this diagnosis."
Technical Architecture
Building multimodal AI requires careful architecture:
1. Unified Data Model
We're designing data structures that treat images and text as first-class citizens. Each case can have:
- Multiple slide images with annotations
- Structured reports with linked regions
- Clinical history and metadata
- Cross-references to similar cases (by image and text)
2. Embedding Space
Both images and text are converted into embeddings (vector representations) in a shared space. This allows:
- Semantic search across both modalities
- Similarity matching between images and text descriptions
- Cross-modal retrieval (find images from text queries, and vice versa)
3. Retrieval-Augmented Generation
RAG systems can query both image and text databases, then generate answers that cite specific slides and reports. This avoids hallucination while enabling natural language interaction.
4. API Integration Points
We're building APIs that allow external AI models to:
- Access both image and text data for training
- Submit multimodal predictions (e.g., "this image region matches this text description")
- Query the unified database for similar cases
The Competitive Advantage
Most pathology platforms are either image-focused (slide viewers) or text-focused (LIS systems). By building multimodal capabilities from the ground up, we're creating a platform that reflects how pathologists actually work: combining visual and textual information seamlessly.
Use Cases
Educational: Case-Based Learning
Students can search for cases using natural language: "Show me cases of breast cancer with lymph node involvement." The system returns slides with matching images AND relevant clinical histories, creating richer learning experiences.
Clinical: Diagnostic Assistance
Pathologists can query: "What's the typical presentation of this morphology in patients with this clinical history?" The system searches both image databases and clinical records to provide evidence-based answers.
Research: Pattern Discovery
Researchers can discover correlations between visual patterns and outcomes by querying across both image and text data. "Do cases with this staining pattern have better outcomes when treated with this protocol?"
The Road Ahead
Multimodal AI is still emerging, but the direction is clear. Major pharma companies are investing heavily because they see the potential: combining visual and textual insights accelerates discovery and improves accuracy.
At NanoView, we're building the infrastructure layer that makes multimodal AI practical. By creating unified data models, embedding spaces, and API integration points, we're positioning ourselves to enable the next generation of computational pathology tools.
The pathologists and institutions that adopt multimodal platforms early will have a significant advantage: faster workflows, better accuracy, and richer insights from their data. We're building the foundation that makes that possible.
The Vision
A platform where pathologists can seamlessly move between images and text, where AI assists without replacing human expertise, and where every case is connected to a rich network of similar cases, research, and clinical context. That's the future we're building.