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rtificial Intelligence (AI) is a branch of computer Asciences that uses learning algorithms to calculate
probability of outcome by using Bayes theorem and
other statistical methods for a given certain input
(Fig.1). When the chance of an event occurring is calculated over and over again after adding new data or
evidence at each step, the probability can reach the level
of near certainty for given inputs. Thousands, even
millions of data points are incorporated in calculating
posterior probability for predictive analytics. The analytics are input neutral as programs predict the future
events irrespective of the type of the data. AI has, thus,
blurred the boundaries between the physical, digital,
and biological worlds. The initial learning process is
considered training where inputs are given to the program already marked for the expected outcome. This
training information can either be highly precise or very
vague allowing different degrees of freedom to the program but also increasing the burden of training. Once
trained an AI algorithm is able to predict or analyze
given input to suggest the required outcome with some
certainty. This improves with continued training through feedback.
Sarwat Hussain, Danish Ejaz Bhatti. (2022) Artificial Intelligence and Medical Education, , volume 28, issue 1.
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