Purpose of Review: Transcranialelectrical stimulation (tES) is used to non-invasively modulate brain activityin health and disease. Current flow modeling (CFM) provides estimates of whereand how much electrical current is delivered to in the brain during tES. Ittherefore holds promise as a method to reduce commonplace variability in tESdelivery and, in turn, the outcomes of stimulation. However, the adoption ofCFM has not yet been widespread and its impact on tES outcome variability isunclear. Here, we discuss the potential barriers to effective, practicalCFM-informed tES use. Recent Findings: CFMhas progressed from models based on concentric spheres to gyri-precise headmodels derived from individual MRI scans. Users can now estimate the intensityof electrical fields (E-fields), their spatial extent, and the direction ofcurrent flow in a target brain region during tES. Here. we consider the multi-dimensionalchallenge of implementing CFM to optimise stimulation dose: this requiresinformed decisions to prioritise E-field characteristics most likely to resultin desired stimulation outcomes, though the physiological consequences of themodelled current flow are often unknown. Second, we address the issue of adisconnect between predictions of E-field characteristics provided by CFMs andpredictions of the physiological consequences of stimulation which CFMs are notdesigned to address. Third, we discuss how ongoing development of CFM inconjunction with other modelling approaches could overcome these challengeswhile maintaining accessibility for widespread use. Summary: Theincreasing complexity and sophistication of CFM is a mandatory step towards dosecontrol and precise, individualised delivery of tES. However, it also riskscounteracting the appeal of tES as a straightforward, cost-effective tool forneuromodulation, particularly in clinical settings
