Improving Viewer Interactions and Looking at Health Chatbots


Hello Reader,

Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below ↓

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Zoom That Works Everywhere

If you can’t zoom any pane in your web DICOM viewer, you’re doing extra work for no reason.

Think of it like this: when something is small, you bring it closer. Same idea in orthopedics, you want the screw, cortex, or joint space closer in whatever you’re looking at (axial, coronal, sagittal, or the 3D view).

What we add to web DICOM viewers:

  • Zoom anywhere: any MPR pane or the 3D render.
  • Linked zoom (optional): zoom in one pane and the others follow. Or turn it off if you want them independent.

This removes a lot of friction:

  • fewer screenshots exported just to zoom
  • fewer maximize/minimize flips
  • fewer pan/zoom repeats across views

You stay with the case, not the controls.

We fold this zoom-anywhere behavior directly into the viewer so teams don’t have to switch tools just to see what matters.

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A Quick Look at Health Chatbots

Health chatbots are becoming more common in care settings. They handle simple questions, guide patients, and reduce some of the workload on clinical teams.

Some tools focus on mental health and daily check-ins. Others act as symptom checkers, helping patients understand what might be going on before they reach a clinician. There are also chatbots designed for medication reminders, preparation steps before procedures, and basic triage decisions.

The idea is not to replace clinicians, but to give patients a first point of contact and help filter routine questions. As these tools improve, they’re becoming more capable of understanding context, offering guidance, and connecting users to the right level of care.


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We Can Help You with Your Next Medical Imaging Project

If your company or organization is looking to build a machine learning solution for a medical imaging problem, then feel free to reach out to us at:

​contact@pycad.co​

We can help you build a full ML solution from training to deployment with affordable rates!

You can check out some of the projects that we worked on here:

​https://pycad.co/portfolio.

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That's it for this week's edition, I hope you enjoyed it!

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Machine Learning for Medical Imaging

👉 Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | 💡 The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!

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