Among the many possible ways to study the right tail of a real-valued random variable, a particularly general one is given by considering the family of its Wang distortion risk measures. This class of risk measures encompasses various interesting indicators such as the widely used Value-at-Risk and Tail Value-at-Risk, which are especially popular in actuarial science, for instance. We start by building simple extreme analogues of Wang distortion risk measures. Special cases of the risk measures of interest include the extreme Value-at-Risk as well as the recently introduced extreme Conditional Tail Moment. Adapted estimators of the resulting extreme Wang distortion risk measures are then introduced when the random variable of interest has a heavy-tailed distribution and their asymptotic normality is shown. The finite sample performance of our estimators is assessed on a simulation study.