Machine Learning in Medical Imaging: Key to Diagnosing Diseases

April 4, 2023

Carolyn Joy V.

There's certainly a lot to be said about how artificial intelligence and machine learning algorithms are transforming the way things are being done in just about every industry. The use of machine learning in the healthcare sector however, is life-changing and, to an extent, life-saving.

More organizations think so, too. The Future of Health Index 2022 Report commissioned by health tech conglomerate Phillips indicates that 60 percent of healthcare leaders are bent on making healthcare AI a priority. The surging interest in AI and machine learning in medicine is not without reason. Already, AI systems have been driving innovation and efficiently handling the rapidly-growing stores of healthcare data. And there's more - machine learning in medical imaging.

Medical Image Annotation for Machine Learning

Evaluating medical images is one of the most significant applications of machine learning in medicine, showing a lot of potential for diagnosing diseases and in-depth image analysis. But to work effectively, ML models for medical imagery require high quality training data―data that you can only get from labeled and annotated images. This is where medical image annotation is needed.

Medical image annotation is the process of labeling medical images such as MRI, PET, and CT scans, X-Ray, ultrasound, and the like. The labeled images that are generated form part of the collection of annotated examples of medical images which are used as training datasets for neural networks in machine learning and deep learning algorithms. Medical image annotations are provided by health specialists in that field to form as a basis for reliable and highly-accurate medical diagnostics.

Machine Learning Use Cases for Medical Images

Massive amounts of data are generated by medical imaging, and when processed with the right annotating tools, these data sets can be harnessed into structured sets that ML algorithms can glean insights from.

That said, here are some of the latest machine learning use cases for medical imagery:

From early tumor detection to diagnosing diseases, to monitoring of disease progression and treatment of chronic disorders, these applications of machine learning in medical imaging are nothing short of transformational for the healthcare sector. And with the rapid development in AI technology, more health innovations and better patient treatment options are not too far off in the future.

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