The powerful impact that Artificial Neural Networks have in the Medical Field.

 Artificial intelligence has the potential to transform the way surgery is taught and practiced.

Although the surgeon-patient-computer relationship's potential is a long way from being fully discovered, the use of AI in surgery is already driving significant changes for doctors and patients alike. AI is currently perceived as a supplement and not a replacement for the skill of a human surgeon.

Deep learning recurrent neural networks (RNN) (Recurrent neural networks are a variant of the conventional feedforward artificial neural networks that can deal with sequential data and can also be trained to hold the knowledge about the past.). Among many other usages in the medical field, RNNs are being used to predict from renal failure in real-time to mortality and postoperative bleeding after cardiac surgery. Using RNNs has obtained improved results compared to standard clinical reference tools. 

Neural Networks are essentially a part of Deep Learning, which in turn is a subset of Machine Learning. ML allows a computer to utilize partial labeling of the data (supervised learning) or the structure detected in the data itself (unsupervised learning) to explain or make predictions about the data Supervised learning is useful for training an ML algorithm to predict a known result or outcome while unsupervised learning is useful in searching for patterns within data.

In supervised learning, human-labeled data are fed to a machine-learning algorithm to teach the computer a function, such as recognizing a gallbladder in an image or detecting a complication in a large claims database. In unsupervised learning, unlabeled data are fed to a machine-learning algorithm, which then attempts to find a hidden structure to the data, such as identifying bright red (e.g. bleeding) as different from non-bleeding tissue.

Sources:

https://www.mobihealthnews.com/news/contributed-power-ai-surgery

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995666/