Fairness considerations have motivated new clustering problems and algorithms in recent years. In this paper we consider the Priority Matroid Median problem which generalizes the Priority $k$-Median problem that has recently been studied. The input consists of a set of facilities $\mathcal{F}$ and a set of clients $\mathcal{C}$ that lie in a metric space $(\mathcal{F} \cup \mathcal{C},d)$, and a matroid $\mathcal{M}=(\mathcal{F},\mathcal{I})$ over the facilities. In addition each client $j$ has a specified radius $r_j \ge 0$ and each facility $i \in \mathcal{F}$ has an opening cost $f_i$. The goal is to choose a subset $S \subseteq \mathcal{F}$ of facilities to minimize the $\sum_{i \in \mathcal{F}} f_i + \sum_{j \in \mathcal{C}} d(j,S)$ subject to two constraints: (i) $S$ is an independent set in $\mathcal{M}$ (that is $S \in \mathcal{I}$) and (ii) for each client $j$, its distance to an open facility is at most $r_j$ (that is, $d(j,S) \le r_j$). For this problem we describe the first bicriteria $(c_1,c_2)$ approximations for fixed constants $c_1,c_2$: the radius constraints of the clients are violated by at most a factor of $c_1$ and the objective cost is at most $c_2$ times the optimum cost. We also improve the previously known bicriteria approximation for the uniform radius setting ($r_j := L$ $\forall j \in \mathcal{C}$).