Welcome to the first episode of The Healthcare AI Podcast, presented by John Snow Labs!
Join John Snow Labs CEO David Talby and CTO Veysel Kocaman, as they crack open the future of medicine.

They’ll reveal how state-of-the-art Healthcare AI is transforming the industry, directly comparing leading Frontier LLMs like OpenAI's GPT-4.5, Anthropic's Claude 3.7 Sonnet, and John Snow Labs’ Medical LLM.

Dive deep into critical clinical tasks, from summarization and information extraction to de-identification and clinical coding. You'll get expert insights from practicing doctors evaluating these models for factuality, relevance, and conciseness, demonstrating which AI truly delivers.

Bonus, understand the significant cost differences and learn why private, on-premise deployment is a game-changer for data privacy and compliance. You'll walk away with a deeper knowledge of the models poised to revolutionize healthcare, ensuring accuracy and compliance in your AI initiatives.

Episode Highlights & Timestamps:

0:00 - Welcome & Episode Overview
0:48 - Benchmarking Frontier LLMs & Clinical NLP
2:00 - The Competitors: OpenAI, Anthropic, AWS, Azure, Google
3:15 - Introducing John Snow Labs Medical LLMs
6:42 - Why AI Evaluation is Critical in Healthcare
9:48 - Blind Evaluation by Medical Doctors: Methodology
15:12 - Overall Preference: John Snow Labs vs. GPT-4.5 & Claude Sonnet 3.7
22:56 - Clinical Information Extraction Benchmarks
27:08 - Advanced NLP: Named Entity Recognition (NER) Deep Dive
29:53 - Assertion Status Detection: the crucial role of context (e.g., patient denies pain vs. father with Alzheimer's) and how different solutions compare in accuracy.
35:37 - Medical Coding with RxNorm: the way of mapping clinical entities to standardized terminologies and the performance metrics for RxNorm.
39:18 - The Clinical De-identification of PHI Data: the most critical privacy use case in healthcare

Listen on:
• ⁠⁠Apple Podcast⁠⁠: https://podcasts.apple.com/us/podcast/the-healthcare-ai-podcast/id1827098175
• ⁠⁠Spotify⁠⁠: https://open.spotify.com/show/2XNrQBeCY7OGql2jVhcP7t 
• Amazon Music⁠⁠: https://music.amazon.com/podcasts


Connect with us:

Our website: https://www.johnsnowlabs.com/
LinkedIn: https://www.linkedin.com/company/johnsnowlabs/
Facebook: https://www.facebook.com/JohnSnowLabsInc/
X: https://x.com/JohnSnowLabs

The Healthcare AI Podcast - Ep.1: Evaluating LLMs on Medical Tasks

Explore regulatory‑grade multimodal data de‑identification and tokenisation with Youssef Mellah, PhD, Senior Data Scientist at John Snow Labs and Srikanth Kumar Rana, Solutions Architect at Databricks. 
Learn how to remove, mask or transform PHI across clinical notes, tables, PDFs and DICOMs at scale, while meeting HIPAA, GDPR and CCPA standards — all without sacrificing analytical value. 

Timestamps
00:00 – Welcome & Episode Overview
02:43 – How Databricks supports secure De‑identification workflows
03:50 – Built-in techniques: masking, encryption, hashing
05:26 – Introduction to Multimodal Data De-Identification
07:15 – OCR + NLP pipeline for visual & text data – PHI Extraction
08:35 – Notebook demo: PHI identification in clinical notes
12:00 – PDF de-identification
12:56 – DICOM file de-identification
14:18 – Output: consistent masking across all modalities

Listen on:
• ⁠⁠Apple Podcast⁠⁠: https://podcasts.apple.com/us/podcast/the-healthcare-ai-podcast/id1827098175
• ⁠⁠Spotify⁠⁠: https://open.spotify.com/show/2XNrQBeCY7OGql2jVhcP7t 
• Amazon Music⁠⁠: https://music.amazon.com/podcasts


Connect with us:

Our website: https://www.johnsnowlabs.com/
LinkedIn: https://www.linkedin.com/company/johnsnowlabs/
Facebook: https://www.facebook.com/JohnSnowLabsInc/
X: https://x.com/JohnSnowLabs

The Healthcare AI Podcast – Ep. 2: De-Identification in Multimodal Medical Data (Text, PDF, DICOM)

Explore real-world applications of generative AI, large language models, and advanced NLP in The Applied AI Podcast. We dive into healthcare, finance, legal, life sciences, and more with expert interviews, practical case studies, and insights on open-source tools and frameworks. Discover how organizations deploy AI at scale, navigate ethical and technical challenges, and unlock transformative business value. Open and impactful discussions for AI professionals and enthusiasts.