John Snow Labs 59d ago

Multimodal clinical data is not a volume problem — it is a meaning problem. Most healthcare AI systems still rely on keywords, rules, and codes that miss what actually happens in clinical text. In this session, Shraddha Gupta (Data Scientist, The Resource Group) explains why clinical language breaks traditional NLP — and how LLM embeddings solve it by capturing meaning instead of matching words. Instead of adding more rules or generative layers, embeddings enable semantic search, patient similarity, and concept normalisation directly on clinical notes — with lower cost, lower latency, and minimal hallucination risk. ⏱️ Key moments: 00:00 Why clinical text breaks traditional AI systems 02:40 The “meaning gap” in healthcare data (codes vs real context) 05:20 What embeddings actually are and how they work 08:30 Embeddings vs RAG vs LLMs — when to use what 09:50 Real-world use cases: search, patient similarity, concept normalisation 📌 Applied Healthcare AI Summit 2026 — what actually works in real-world healthcare AI, from pilots to production systems. #HealthcareAI #ClinicalAI #LLMEmbeddings #MedicalNLP #ClinicalData #AIinHealthcare #SemanticSearch #PatientData #AIGovernance #HealthTech