Medicine

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.”


related case studies

Contextflow

Contextflow uses Julia to help radiologists prioritize and diagnose difficult cases faster and more accurately

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Riken

Riken uses BioJulia for RNA sequencing

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Deep Learning for Medical Diagnosis

Deep learning used to diagnose diabetic retinopathy

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Augmented Reality Gives Surgeons ‘X-Ray Vision’

Augmedics uses CT scans to give surgeons a 3D image of patient anatomy

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Precision Medicine

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related packages & products

BioJulia
Bioinformatics and Computational Biology Infrastructure for Julia

Pumas
PharmaceUtical Modeling And Simulation

RLEVectors
Run Length Encoded vectors for julia, inspired by BioConductor

Gillespie
Stochastic Gillespie-type simulations using Julia

Mamba
Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia

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