Data
Multimodal AI
Multimodal AI refers to AI systems that process and generate multiple types of data — text, images, audio, video, and structured data — within a single model or integrated pipeline. Frontier multimodal models (Claude, GPT-4o, Gemini) can analyze documents with charts, interpret medical images, transcribe audio, and reason across modalities simultaneously.
Multimodality unlocks business use cases that were impossible with text-only models: invoice processing that reads both text and table layouts, clinical documentation that combines transcribed speech with EHR data, and customer support that analyzes product photos alongside written complaints. As of 2026, image and document understanding are production-ready; video understanding is emerging; real-time audio processing is available in select models.
Related terms
- LLM (Large Language Model) — A large language model (LLM) is a neural network trained on broad text corpora that can generate, summarize, translate, classify, and reason about natural language.
- Intelligent Document Processing — Intelligent document processing (IDP) is the automated extraction of structured data from unstructured or semi-structured documents — invoices, contracts, clinical notes — using OCR, NLP, and large language models, then routing the data to downstream systems.
- Ambient AI Scribe — An ambient AI scribe is a system that records the patient encounter (with consent), transcribes the conversation, and produces a structured clinical note for clinician review.
- RAG (Retrieval-Augmented Generation) — RAG is a pattern in which an AI model retrieves relevant documents from a knowledge base at query time and uses them as additional context to generate its response.