Medical Image Processing using AI

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September 19, 2022


AI / Healthcare


Medical Image Processing using AI

Artificial intelligence (AI) has been widely documented in healthcare, and medical imaging is one of its most promising applications. The data from the images provide clinicians with an abundant and intriguing source of information about patients. The computerized algorithms and pattern recognition abilities of AI are increasingly helping medical practitioners make an accurate diagnosis and treatment plan. The most common use of medical imaging has been done to detect cancer, and AI can take this technology forward. Machine learning algorithms’ various applications include image analysis, identifying different types of cancer, interpreting diagnostic images such as X-rays, CT, and MRI, predicting diseases like diabetes, stress, Alzheimer’s disease, etc.


Research has shown that AI tools can perform diagnosis with impressive accuracy by recognizing characteristics and even minute variances of medical images quickly and accurately than medical practitioners. The benefit lies in the fact that automating the identification of abnormalities in commonly done imaging tests, such as x-rays, can help the clinician with faster clinical decision-making and reduces the rate of errors.

One of the most significant advantages of AI is its ability to assess a vast number of images in a quicker and more error-free manner than a clinician. This could also reduce and prevent medical errors and overuse of testing and be beneficial for diagnostic accuracy. ML-enabled Simplification of the clinician and radiologist’s workflow comes as a boon during the hectic clinician duty hours—moreover, the AI-based algorithms help practitioners develop new classification and pattern recognition. Decisions based on computers are more objective, and this can improve patient outcomes.

To assist physicians with improved decision making, numerous startups are pursuing breakthrough solutions. For example, Viz helps doctors recognize abnormalities through machine learning in brain scans, while Neural Analytics produces a robotically assisted ultrasound system for evaluating brain health. Likewise, VoxelCloud has computer-aided detection systems to detect and diagnose different diseases, ranging from cardiovascular and lung diseases to eye diseases. Their alternatives are focused on state-of-the-art computer vision, deep learning, and technology for artificial intelligence.

To promote imaging data analysis, teleradiology, and artificial intelligence startups such as Nines and Braid Health provide an investigational machine learning framework. To construct and use mathematical patient representation models for diagnostics and disease risk assessment, Botkin.AI uses artificial intelligence.

While Prognica is developing a smart screening solution for early detection of breast cancer using artificial intelligence and machine learning, is developing an Artificial Intelligence-powered healthcare predictive analytics system, particularly for prostate cancer, and Endogene has developed a single-use gastrointestinal endoscope platform for self-advancement.


Like every other technology, AI also has several challenges and stumbling blocks. The biggest challenge is the need for massive high-quality datasets for training the computer. Augmentation may help solve this issue question where the available data and images can be increased manifolds while the new images retain the same characteristics. However, these new images are slightly different, so that it is considered a “new training material” by the neural network.

Every medical image has the possibility of having many findings and diagnoses. When a radiologist sees a medical image, several interpretations and differential diagnoses consider, but when the same image is fed to the computer, the available AI algorithms currently focus only on a single finding on these images. Some argue that this may enhance diagnostic speed and accuracy in identifying diseases, but since the algorithm can locate only one’ problem’, there is a need to generate a purpose-built algorithm every time a new finding has to be identified. Generalized algorithms that relate to large sets of scenarios are challenging to create. Moreover, the computer-generated diagnostics algorithms can spot a pattern variance in the medical image, but unlike clinicians, it cannot explain the reason for its occurrence to the radiologist.

Furthermore, simple two-dimensional images are currently used to train deep learning models, while in reality, the images clinicians investigate, such as CT and MRI images, are three-dimensional. Hence the deep learning models available now are not adjusted to these, and one has to keep this in mind while applying deep learning to these types of images.
Lack of standardization during image acquisition is yet another challenge. Large datasets taken using standardized methods are needed to ensure proper training and accurate algorithm. Sometimes, massive data inputs to learn rules to evaluate new images can make machine-learning algorithms complicated and tricky.

Most of the studies done so far are retrospective. Prospective studies are needed to understand the performance and accuracy of using real-world data. There is also a common notion that introducing machine learning into clinical diagnostics would cause computers to replace clinicians and radiologists. Moreover, there is a possibility of legal issues if an algorithm gives out a wrong diagnosis or medical error.

In conclusion, AI is revolutionizing the way clinicians interpret medical images and diagnose disease. While there are several benefits, there are many issues that need to be tackled. Clinicians need to get trained to understand AI algorithms, medical image dataset sizes, and the need to standardize the acquisition of images. However, deep learning networks will get better and better at detecting image deviations, making them an outstanding instrument in the future for understanding and interpreting medical images.

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