Collaborating Authors

Machine Learning Engineer - The Machine Learning Conference


Overjet is an early-stage VC-backed startup building the future of data-driven dentistry. We are using AI to transform the $130B dental care market and improve patient outcomes. We are seeking an entrepreneurially-minded a highly skilled developer who is comfortable with backend software development including deploying machine learning models, loves challenges and is passionate about impacting lives. Please email your resume to Develop machine learning pipelines Deploy machine learning models for inference Implement and maintain metrics for tracking ML models performance Design and develop microservices and APIs related to data ingestion, machine learning and product quality Ensuring responsiveness of applications.

Innovations In: AI and digital health


Over the next decade artificial intelligence is likely to transform the biomedical world. Deep-learning algorithms could aid in developing new drugs, interpreting medical images, cleaning up electronic patient charts, and more.

Adversarial attacks on medical machine learning


With public and academic attention increasingly focused on the new role of machine learning in the health information economy, an unusual and no-longer-esoteric category of vulnerabilities in machine-learning systems could prove important. These vulnerabilities allow a small, carefully designed change in how inputs are presented to a system to completely alter its output, causing it to confidently arrive at manifestly wrong conclusions. These advanced techniques to subvert otherwise-reliable machine-learning systems--so-called adversarial attacks--have, to date, been of interest primarily to computer science researchers (1). However, the landscape of often-competing interests within health care, and billions of dollars at stake in systems' outputs, implies considerable problems. We outline motivations that various players in the health care system may have to use adversarial attacks and begin a discussion of what to do about them.

Open Source Site Aims to Boost Use of Machine Learning 7wData


A vendor with experience and products in data warehousing and analytics is creating a community for open-source software intended to streamline efforts to use machine learning applications in healthcare. Health Catalyst has created as a repository of healthcare-focused open source machine learning software, saying that it's important for the industry to benefit from the technology and democratize machine learning in healthcare. In addition to creating the web site and contributing its tools and algorithms to the open-source community, the company is offering ongoing support to maintain it. The Salt Lake City-based company says the site will provide one site to download algorithms and tools, contribute code, read documentation and communicate with other healthcare professionals who are interested in using the technology to improve patient care. The initiative hopes to spread interest and use of machine learning and artificial intelligence by serving as a repository for coding work in the healthcare arena.

First FDA Approval For Clinical Cloud-Based Deep Learning In Healthcare


The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole. Arterys's medical imaging platform has been approved to be put into use to help doctors diagnose heart problems. It uses a self-teaching artificial neural network which has learned from 1,000 cases so far, and will continue to improve its knowledge and understanding of how the heart works with each new case it examines. In order to be approved by the US Food and Drug Administration (FDA), it had to pass tests to show it can produce results at least as accurately as humans are currently able to. The key difference though is that Arterys takes an average of 15 seconds to produce a result for one case, which a professional human analyst would expect to spend between 30 minutes to an hour working on.