Al algorithms have shown remarkable breakthroughs in several areas in the scientific field; one of which, is tasks of image-recognition. AI is implemented in various methods when it comes to imaging; from neural networks to auto-encoding, and it is moving at a rapid pace, as methods keeps progressing and more applications are becoming more useful. In radiology, physicians can assess medical images for several procedures when it comes to disease: detection, characterization or monitoring. Implementation of AI methods are quite useful at providing quantitative assessments which radiographic characteristics. AI allows machines to interpret and present complex and complicated data. 

One of the methods that AI is based on is deep learning. Deep learning is a part of machine learning and is based on a neural network structure, which is based on and represents the human brain. These algorithms learn patterns and can extract data automatically by possessing the ability to approximate complex nonlinear relations, as they are able to match humans in task-specific applications. And with growing advances in AI research, some of the deep learning methods have even defeated the human brain: for example in the strategies of board games and chess. This is quite the achievement as it proves that machines can eventually play a much bigger role in the medical and research field.

As researchers are predicting that AI will ultimately automate various tasks, from translation of languages to performing surgeries, all expected to happen in the next few decades.

In the field of radiology, physicians visually examine medical images input and then go on by reporting their findings as go detect a disease or characterise it. Their assessment is based on education and expertise, so it is in a way, subjective. So the role of AI excels at the recognition of complex patterns of data imaging, thus providing quantitative assessments in an automated manner.

AI-made assessments are worked in the clinical workflow, functioning as a much helpful tool to physicians.

Imaging data are usually collected within a routine clinical practice. Large data sets are already available and ready to be used, therefore it offers can incredible resource for both medical and scientific discoveries. Radiographic images, combined with clinical data outcomes, have led to a rapid expansion of radiomics, which is a field in medical research. Radiomics studies incorporate deep learning methods and techniques as to learn feature representation automatically.

Many efforts were exerted in oncology to explore radiomics tools for assistance and those efforts were successful. They assist in decision making when it is related to diagnosis and risk of different types of cancer. One example is the studies done in non-small-cell lung cancer, they used radiomics to predict distant metastasis in lung adenocarcinoma and tumor histological types.

Those findings motivated the exploration of AI clinical utility generated biomarkers which are based on standard of care radiographic images, which better supports radiologists and doctors in diagnosis of diseases, quality of images optimization, visualisation of data and much more.

AI keeps paving its way into the medical and research fields, and the results keep getting better.