Protecting Sensitive Data with AI in Healthcare

11min Snip

00:00

Play full episode

Summary

Transcript

Episode notes

This chapter explores the difficulties in safeguarding personal health information (PHI) using AI models, focusing on overcoming false positives and unknown false negatives. It emphasizes the need for accurate risk assessment and vulnerability detection, along with challenges in data labeling, model training, and optimizing AI models for healthcare environments.

We’ve all heard about breaches of privacy and leaks of private health information (PHI). For healthcare providers and those storing this data, knowing where all the sensitive data is stored is non-trivial. Ramin, from Tausight, joins us to discuss how they have deploy edge AI models to help company search through billions of records for PHI.

Leave us a comment

Changelog++ members save 5 minutes on this episode because they made the ads disappear. Join today!

Sponsors:

Neo4j – Is your code getting dragged down by JOINs and long query times? The problem might be your database…Try simplifying the complex with graphs. Stop asking relational databases to do more than they were made for. Graphs work well for use cases with lots of data connections like supply chain, fraud detection, real-time analytics, and genAI. With Neo4j, you can code in your favorite programming language and against any driver. Plus, it’s easy to integrate into your tech stack.
Backblaze – Unlimited cloud backup for Macs, PCs, and businesses for just $99/year. Easily protect business data through a centrally managed admin. Protect all the data on your machines automatically. Easy to deploy across multiple workstations with various deployment options.

Featuring: