Inside Precision Medicine February 1, 2024
Anita Chakraverty

Middle Eastern researchers have designed a secure, machine-learning algorithm that they believe can protect the privacy of omic data for patients.

Omics are new, comprehensive ways to analyze complete genetic or molecular profiles such as the genomes or proteomes of an organism.

A deluge of such data have been generated from massive research projects in the past few decades using high-throughput sequencing platforms.

But this has led to concerns over individual privacy and the potential that data could leak and lead to ethical problems like genetic discrimination. Risks are posed, not just by the data itself, but also through a wide scope of machine learning applications, particularly deep learning, and its use in diverse areas such as genomics, medical...

Today's Sponsors

LEK
ZeOmega

Today's Sponsor

LEK

 
Topics: AI (Artificial Intelligence), Healthcare System, Patient / Consumer, Privacy / Security, Provider, Technology
Privacy concerns mount as Elon Musk's Grok takes on health data
DHS intros framework for AI safety and security, in healthcare and elsewhere
Why Modern Developers Must Master The Balance Of Privacy And Functionality In Mobile Apps
Navigating Security and Privacy Challenges in Healthcare IT: A Strategic Approach
Balancing Personalized Targeting with Protecting Consumer Privacy

Share This Article