Researchers and clinicians use Julia for medical research, diagnosis and treatment
Researchers and clinicians use Julia for medical research, diagnosis and treatment. Julia’s speed and ease of use make it the ideal language for medical applications including diagnosis, identifying new treatments, precision medicine and more.
UK cancer researcher Marc Williams explains:
“Coming from using Matlab, Julia was pretty easy, and I was surprised by how easy it was to write pretty fast code. Obviously the speed, conciseness and dynamic nature of Julia is a big plus and the initial draw, but there are other perhaps unexpected benefits. For example, I’ve learned a lot about programming through using Julia. Learning Julia has helped me reason about how to write better and faster code. I think this is primarily because Julia is very upfront about why it can be fast and nothing is hidden away or “under the hood”. Also as most of the base language and packages are written in Julia, it’s great to be able to delve into what’s going on without running into a wall of C code, as might be the case in other languages. I think this is a big plus for its use in scientific research too, where we hope that our methods and conclusions are reproducible. Having a language that’s both fast enough to implement potentially sophisticated algorithms at a big scale but also be readable by most people is a great resource. Also, I find the code to be very clean looking, which multiple dispatch helps with a lot, and I like the ability to write in a functional style.”
Contextflow uses Julia to help radiologists prioritize and diagnose difficult cases faster and more accurately
Riken uses BioJulia for RNA sequencing
Deep Learning for Medical Diagnosis
Deep learning used to diagnose diabetic retinopathy
Augmented Reality Gives Surgeons ‘X-Ray Vision’
Augmedics uses CT scans to give surgeons a 3D image of patient anatomy
Path BioAnalytics is developing new ways to personalize medical treatment for individual patients
Bioinformatics and Computational Biology Infrastructure for Julia
Pharmaceutical Modeling And Simulation
Run Length Encoded Vectors for Julia, Inspired by BioConductor
Stochastic Gillespie-Type Simulations Using Julia
Markov Chain Monte Carlo (MCMC) for Bayesian Analysis in Julia
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