AI and Health Insurance: A Mixed Future Ahead


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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AI & Health Insurance Claims: Helpful Future or Hidden Risk?

AI is becoming a key part of how insurance companies handle medical claims, and a recent article offered a clear overview of what’s improving and what remains worrying.

AI systems are now used to verify documents, read medical notes, compare with previous claims, and even decide whether a treatment should be approved. And while this can make the process faster, several concerns are growing:

  • Lack of transparency: insurers don’t reveal how their algorithms make decisions.
  • High error rates: some AI tools reportedly deny care incorrectly, with many decisions later reversed.
  • Risk of bias: chronic patients and vulnerable groups may face more denials.
  • Financial incentives: automation can make it easier for insurers to prioritise cost savings over patient care.

Some states in the U.S. have started regulating this, requiring human oversight and more patient-centred criteria. There are also new AI tools helping patients draft appeal letters, an interesting twist.

Overall, AI will play a bigger role in insurance claims. Whether it becomes helpful or dystopian depends entirely on how it’s governed.

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Feature Spotlight: Measure Surface Areas in Seconds

Analyzing a specific region inside your scan shouldn’t feel complicated, and now, it doesn’t.

With our new surface area measurement tool, you can outline any region of interest directly inside the viewer. Just draw a polygon on the slice you’re viewing (axial, sagittal, or coronal) and confirm with a right-click. The area appears instantly.

From there, you’re fully in control:

  • Toggle the measurement on or off
  • Delete it whenever you want
  • Switch units (mm², cm²… your choice)

It’s simple. It’s fast. And it brings powerful regional analysis right into your workflow, no exporting, no extra steps.


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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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