AI is a very powerful tool. It’s currently paving the road to outstanding achievements in many fields, one of them is medicine. Artificial intelligence can be applied in several ways to overcome certain limitations and ease several procedures for both researchers and doctors.

AI can be applied in various methods and techniques; mimic the human brain, designing of drug formulas, assisting in clinical diagnosis and establishing medical statistics databases. Through Deep Learning methodologies, AI is implemented within information management tools, which allows it to interpret information datasets, maintaining electronic health records and extracting necessary information from them in no time. This process is done through mathematical algorithm, which improves learning through experience, much like the human brain.

Logistic data mining and deep learning of clinical diagnostics are both used to empower and enhance Machine learning to facilitate doctors and physicians make important decisions for choosing a course of treatment. Since AI defeated World Chess champion Kasparov in 1997, it had gained the acceptance amidst the scientific community and AI today is believed to be capable of resolving complex problems and biological issues, it is even being used in robotic surgery of cardiac valve repair. AI is also believed to play an important part in the fight against cancer.

Machine learning algorithms have three classifications: supervised, Unsupervised, and Reinforcement. First, supervised learning is used in the prediction algorithms which are based on previously given information. Second, unsupervised learning is used to find hidden patterns without labelling responses and third, reinforcement learning is used as a consequence for an action; like a reward or a penalty, much like video game models. Both molecular medicines as well as genetics by computational biology algorithms and information management have had many discoveries which broadened the impact of AI use in medicine vastly.

Unsupervised algorithms has achieved quite impressive milestones in the discovery of therapeutic targets. In Scotland for instance, they have AI-based clinical assessment services, which allows people who suffer from minor health issues check their condition from the comfort of their home; this AI system is based on DL 111 algorithm, which is implemented in the clinical testing phase.

AI is also used in online healthcare providers that give digital medical services through semantic  technology, which is designed as to allow the data available on the internet readable for machines.

Medical services that are based on AI have developed Linked Data Graphs or LDGs that integrate bio-information based biomedical data banks, which transforms it into simple enough information that can be understood for a normal person.

Massive Data which is related to genetics, radiology and microbiology can be done with computational assistance, as it can be collected and managed in a systematic way. Excellent accuracy when determining infection related carcinogenesis has been done through machine algorithms as well as casual probabilistic network tools, giving outstanding accuracy and are recommended for suitable therapeutic strategies.

Large scale machine learning algorithms is now the main focus of clinical researchers. These algorithms give the machine or computer abilities to learn and discover new drugs through huge pharmaceutical data. These ML algorithms can discover new drug formula in a much shorter time and with much less cost, this is done by using super computer and ML learning tools. It has been proved that Deep Learning derived Machine Learning models did in fact outperform all of the other comparative strategies when they are applied to the databases of pharmaceutical companies.

Artificial intelligence will revolutionise health care as it can make it safer, faster and even more accurate. The established datasets are massive and they continue to grow and impact medical sector.