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Images from MRI machines, CT scanners, and X-rays can contain large amounts of complex data that can be difficult and time consuming for humans to evaluate. It should come as no surprise - this is the trained eye of the specialist that rendering a diagnosis dramatically depends on. Given the many different types and subtypes of disease and the avalanche of new data in the form of various biomarkers and genomics data, this is becoming an increasingly difficult task for the doctor.
Leveraging the emerging capability of AI and machine learning can be of help here. AI-based approaches in healthcare enable researchers to see what may slip from the human eye. AI devices can scan images down to individual pixels, offering precise analytics.
On a different note, many diagnostic processes nowadays still rely on physical tissue samples obtained through biopsies. This process carries risks, including the potential for infection. Radiological images captured by MRI machines, CT scanners, and x-rays can offer non-invasive visibility into the inner workings of the human body. Experts predict that artificial intelligence will enable the next generation of radiology devices, reliable and detailed enough to replace the need for tissue specimens in some cases. To achieve very close registration, the ground truth for any given pixel should be known.
Succeeding in this research may allow clinicians to develop a more accurate understanding of how tumors behave as a whole instead of basing treatment decisions on the properties of a small segment of the malignancy. Researchers may also be able to better define the aggressiveness of cancers and target treatments more appropriately.
That said, artificial intelligence is helping to enable “virtual biopsies” and advance the innovative field of radiomics, which focuses on harnessing image-based algorithms to characterize the phenotypes and genetic properties of tumors.
https://www.zfort.com/blog/ai-in-healthcare
Leveraging the emerging capability of AI and machine learning can be of help here. AI-based approaches in healthcare enable researchers to see what may slip from the human eye. AI devices can scan images down to individual pixels, offering precise analytics.
On a different note, many diagnostic processes nowadays still rely on physical tissue samples obtained through biopsies. This process carries risks, including the potential for infection. Radiological images captured by MRI machines, CT scanners, and x-rays can offer non-invasive visibility into the inner workings of the human body. Experts predict that artificial intelligence will enable the next generation of radiology devices, reliable and detailed enough to replace the need for tissue specimens in some cases. To achieve very close registration, the ground truth for any given pixel should be known.
Succeeding in this research may allow clinicians to develop a more accurate understanding of how tumors behave as a whole instead of basing treatment decisions on the properties of a small segment of the malignancy. Researchers may also be able to better define the aggressiveness of cancers and target treatments more appropriately.
That said, artificial intelligence is helping to enable “virtual biopsies” and advance the innovative field of radiomics, which focuses on harnessing image-based algorithms to characterize the phenotypes and genetic properties of tumors.
https://www.zfort.com/blog/ai-in-healthcare