Our Mission

16 Bit's vision is to augment physicians' diagnostic ability with artificially intelligent medical image analysis systems. Its ultimate goal is to improve the quality and accessibility of healthcare for all patients.


16 Bit is a Toronto-based company founded by two radiology physicians with strong backgrounds in computer science and engineering.  With a unique combination of medical and technical expertise, 16 Bit focuses on solving the most impactful clinical problems facing diagnostic imaging today.


Vast Medical Imaging Data

Partnerships with a wide variety of world-renowned academic and community hospitals as well as private clinics gives us the raw materials to achieve our mission.


Radiology Expertise

Practical expertise in radiology enables us to focus on the most pressing and solvable diagnostic imaging problems we face today.


Advanced Networks and Computation

Amidst a revolution in artificial intelligence, we design custom neural network architectures and train them using state-of-the-art hardware.


We are working in the following disease areas to assist radiologists and clinicians to provide better care.

Breast Cancer Screening

1 in 8 women will be diagnosed with breast cancer in their lifetime. Mammography is a well-established modality used to screen for breast cancer. 16 Bit is working on a screening algorithm to triage mammograms and assist radiologists in their interpretation.

Neurological Emergency Detection

Computed tomography of the head is a commonly performed test done to exclude acute neurological abnormalities such as bleed and stroke. 16 Bit is validating a proprietary architecture to analyze a volumetric CT acquisition of the head and exclude acute findings.

Pediatric Bone Age

Pediatric populations frequently require correlation between skeletal maturation and chronological age. Currently, radiographs of hands are used and patients are categorized based on the presence and absence of bony features. This method is dated and cumbersome. 16 Bit participated in the 2017 RSNA Machine Learning Challenge and achieved 1st place.

Try our bone age model


Find out about our mission, products and meet our great team.


Machine Learning and the Future of Radiology: How we won the 2017 RSNA ML Challenge

A deep-dive into how we trained a neural network to predict pediatric bone age with a mean absolute difference of 4.265 months.

Read more


Helping us revolutionize medical diagnosis

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Bits About Us



Get in touch if you would like to partner with us or if you have any questions or comments.