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Unstructured visual data—from handwritten intake forms to pathology slides—remains a massive bottleneck in healthcare. This webinar introduces new benchmarks for John Snow Labs’ Medical Visual Language Models (vLLMs), demonstrating how they transform complex clinical imagery and documents into structured, regulatory-grade JSON in seconds.

We will present a comparative analysis across 10 specialized medical use cases:

1. Visual De-identification: Auto-redacting PHI from scanned clinical forms.
2. Handwritten Rx Extraction: Field-level data capture from diverse prescription formats.
3. Automated Chart Sorting: Organizing and indexing heterogeneous patient record piles.
4. Intake Form Routing: Directing handwritten forms to departments based on clinical urgency.
5. Dermatology Similarity: Case matching for skin lesion classification.
6. Radiology Archive Search: Semantic retrieval across vast chest X-ray databases.
7. Pathology Slide Benchmarking: Supporting rare diagnosis workflows via visual similarity.
8. ECG Waveform Analysis: Extracting digital metrics from scanned paper tracings.
9. Visual RAG: Designing semantic search pipelines over clinical document archives.
10. Longitudinal Case Synthesis: Finding similar patient profiles using multimodal historical records.

What you’ll learn:

  • Performance benchmarks for zero-shot structured extraction across medical modalities.
  • RAG pipeline architecture for semantic and visual similarity in clinical decision support.
  • Quantified ROI: Reductions in processing time and error rates vs. manual review.
  • Immediate access to all code, notebooks, and models via GitHub.
Christian Kasim Loan

Christian Kasim Loan is a Lead Data Scientist and Scala expert at John Snow Labs and a Computer Scientist with over a decade of experience in software and worked on various projects in Big Data, Data Science and Blockchain using modern technologies such as Kubernetes, Docker, Spark, Kafka, Hadoop, Ethereum, and overr 20 programming languages to create modern cloud-agnostic AI solutions, decentralized applications, and analytical dashboards.

He has deep knowledge of Time-Series Graphs from his previous research in scalable and accurate traffic flow prediction and working on various Spatio-Temporal problems embedded in streaming graphs at a Daimler lab.

Before his graph research, he worked on scalable meta machine learning, visual emotion extraction, and chatbots for various use cases at the Distributed Artificial Intelligence lab (DAI) in Berlin.

His most recent work includes the NLU library, which democratizes 10000+ state-of-the-art NLP models in 200+ languages in just 1 line of code for dozens of domains, with built-in visualizations and all scalable natively in Spark Clusters by its underlying Spark NLP distribution engine.

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