Medical AI Companies Are Succeeding Because of This

We are seeing more and more AI companies in the Medical Imaging space. A lot of them are leveraging AWS to build their product.

So when clients in this space reach out to us at PYCAD to help them build their solutions, we made sure to take advantage of what AWS has to offer.

For example, exploring AWS SageMaker's asynchronous inference has opened new avenues for our medical imaging AI projects that we built for our clients.
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It allows us to handle voluminous data sets like DICOM files efficiently, without the constraints of real-time processing.
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This capability ensures our AI models can delve deeply into complex imaging data, improving diagnostic accuracy.
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We've also adopted Docker containers for deploying our AI models in healthcare, especially in medical imaging. It has been transformative.
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The consistency and isolation provided by Docker, combined with AWS SageMaker, enhance our deployment pipeline, making it seamless and efficient.
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This approach ensures our AI models are robust and reliable for critical healthcare applications.

How to Deploy your Medical AI Models on AWS

3 types of deployment endpoints available on SageMaker that help us deploy medical imaging AI models.

AWS SageMaker offers a versatile suite of endpoint types to cater to diverse deployment needs, ensuring that medical AI solutions are scalable, reliable, and fit for purpose.

Let’s dive into the types of endpoints SageMaker supports and how they can revolutionize healthcare AI applications:

• Real-Time Endpoints:

Ideal for applications requiring immediate responses, real-time endpoints serve predictions on-demand for clinical decision support tools, enhancing diagnostic workflows with speed and accuracy.

• Asynchronous Endpoints:

When dealing with large volumes of data, such as high-resolution whole-slide images or 3D scans, asynchronous endpoints allow for processing without the constraints of a synchronous response, ensuring comprehensive analysis without timeouts.

• Batch Transform Jobs:

For offline predictions on large datasets, batch transform jobs efficiently process bulk data. This is perfect for periodic analysis of archived medical images, enabling retrospective studies and research with ease.

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X Post of the Day

New AI model for medical imaging data classification!

Khan M. Siddiqui, MD @drkhan Diving into the future of #MedicalImaging with MedMamba 🐍💡 - where convolution meets SSM for advanced image classification. It’s a step towards more precise and efficient diagnostics. #AI #HealthTech #Innovation ⁦@woojinrad⁩ thoughts? https://arxiv.org/abs/2403.03849 arxiv.org MedMamba: Vision Mamba for Medical Image Clas... Medical image classification is a very fundamental and crucial task in the field... 6:29 PM • Mar 8, 2024 4 Retweets 18 Likes Read 2 replies

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