Although the above-mentioned efforts have demonstrated the potential for state-of-the-art machine learning to meet existing challenges for APT data processing, some major aspects remain to be tackled to fulfil the full potential. Machine-learning has the potential to help address many of APT shortcomings, and, for instance, resolve some aberrations that plague the accuracy of the measurements by better interfacing with modelling efforts in APT. This is necessary to reach true atomic-resolution that will help extract more precise local atomic arrangements. Optimisation of the data acquisition, and establishing a dialog between the instrument and the data processing are also areas that will need exploring -in this regard, APT is far behind other high end microscopy techniques. Overall, we are only at the beginning of the use of machine-learning for APT, but the preliminary work that has been done across the community lays solid ground to build better, more encompassing and efficient tools in the future.