# Topics > Medical robotics and AI >  Improving diagnosis with computer vision applications in healthcare

## oodlesai

Artificial Intelligence (AI) is showcasing transformative potential to streamline and improve therapeutic and healthcare practices. From predictive analytics to computer vision, AI technologies are breaking through traditional medical techniques to accelerate and scale multiple treatments. In an attempt to contribute to the current pandemic, Oodles AI is exploring how computer vision services can advance healthcare facilities effectively. Here are some data-driven computer vision applications in healthcare that deploys machine learning algorithms for diverse medical purposes.

*Medical Image Recognition
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From X-rays to CT scans and MRIs, medical professionals are required to examine complex images to analyze patient health and initiate diagnosis. Diagnostic imaging techniques are critical for qualitative as well as quantitative visual assessment to deliver various healthcare services. However, manual efforts are inefficient at identifying microscopic anomalies in medical images accurately.

The advent of AI has revolutionized image processing technologies such as computer vision. Leading providers of artificial intelligence services are innovating traditional practices with computer vision for optimizing and scaling treatment across specialties.

Process Segmentation of Medical Images
Image segmentation is one of the most essential medial processing practices used to extract the region of interest (ROI) in body organs or tissues.

Over time, simple equations are turning obsolete at modeling crucial objects such as lesions and anatomy in medical images accurately. Advancements in machine learning algorithms are breaking the barriers across indispensable medical imaging processes such as-

a) Computer-aided diagnosis for lung, breast, and liver cancer along with acute and chronic diseases.

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## thugsforlife

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