Medical imaging helps physicians save lives. But there is a challenge in medical imaging; the images’ complexity will see 25 percent of patients experience a false positive. When the radiologist and physician are overworked, the image readings they give are subjective, especially given the complexity of images and the existence of different interpretations from the same image.


False negatives are less common but more catastrophic. Diagnosing a disease late will increase treatment costs and reduce the rates of survival. The application of AI in image analysis will improve the accuracy of outcomes drastically.


How Does AI Image Analysis Work?


AI uses complex algorithms to analyze images for lesions and tumors. Unlike the manual process, advanced AI algorithms can identify the tiniest of tumors. When the machine receives a new image, it compares the normal image with the provided image to identify any abnormalities. As these algorithms advance, early detection of diseases such as cancer will become easy.


By using AI, clinicians get a diagnosis in less than half the time the manual process takes and with increased accuracy.


Even with the many advantages of machine learning, it is challenging to implement for everyday use. Challenges come in the complex nature of developing the algorithms, the testing, and getting FDA approval. Even after approval, radiologists also need to distribute the systems, train other radiologists and clinicians, and regularly update the algorithms.


The accuracy of AI models is dependent on data. Most of the existing models are creations in controlled settings where the data fed is not nearly enough to guarantee accurate results. The models might not work seamlessly when applied to data from other locations, with different populations and different machines.


AI model developers need to create algorithms that seamlessly integrate into the workflow of radiologists. These algorithms should be able to analyze thousands of sets of data to give an accurate diagnosis.


The future of Machine Learning in Medical Imaging


In medical imaging, algorithms analyze shape, colors, and texture associated with different diseases. The machine can analyze billions of images to identify patterns and reduce the instances of false positives or negatives. In the future, the algorithms will be curated to work with different sets of data and different imaging machines and still offer accurate and fast results. For now, any institution implementing machine learning does it as a complement to radiologists – but that might change in the future.