A combination of machine learning algorithms and recurring patterns of health conditions serve as the fuel for AI to drive the future of medical practices. Medicine and machine learning aren’t necessarily relative or coherent concepts, but a combination of the two results in an uncanny but surprisingly beneficial application that might just propel the healthcare industry in the near future. Since its conceptualization, AI has been associated with numerous applications that involve deep learning algorithms recognizing patterns of disease spread and potential threats.

Google, in its recent quest to develop AI for healthcare applications, was able to train AI models to detect the spread of breast cancer tissues on a microscopic specimen. Surprisingly, the AI was able to predict the infection rate faster than human pathologists. On similar lines, studies have shown significant success in containing diabetes by learning the food habits of an individual and the severity of the disease through retinal scans. Owing to multiple data sets fed into AI models, machine learning algorithms can predict similarities within the data sets and arrive at various possibilities by cross-referencing inter-relatable data. In a recent study, AI was used to successfully diagnose the likelihood of tuberculosis through X-ray image analysis with incredible accuracies. Researchers trained the AI model to learn from similar X-ray patterns and compare the newly fed samples for resembling patterns. The outcome—96 percent accuracy. Researchers concluded that automated detection could be a breakthrough in the field of medicine which would extend the possibility for early containment and cure.