  Determining physical properties inside an object without access to direct
measurements of target regions can be formulated as a specific type of
\textit{inverse problem}. One of such problems is applied in \textit{Electrical
Impedance Tomography} (EIT).
  In general, EIT can be posed as a minimization problem and solved by
iterative methods, which require knowledge of derivatives of the objective
function. In practice, this can be challenging because analytical closed-form
solutions for them are hard to derive and implement efficiently.
  In this paper, we study the effectiveness of \textit{automatic
differentiation (AD)} to solve EIT in a minimization framework. We devise a
case study where we compare solutions of the inverse problem obtained with AD
methods and with the manually-derived formulation of the derivative against the
true solution.
  Furthermore, we study the viability of AD for large scale inverse problems by
checking the memory and load requirements of AD as the resolution of the model
increases. With powerful infrastructure, AD can pave the way for faster and
simpler inverse solvers and provide better results than classical methods.
