Radiopharmaceutical therapy (RPT) plays an important role in modern precision oncology. However, treatment activity is still prescribed by fixed-dose protocols rather than adjusted to planned patient-specific absorbed dose (AD). Individualized dosimetry holds promise for improving treatment planning but is not yet standard. One main reason is that present dosimetry workflows depend on repeated imaging at multiple time points and require kinetic modeling and computational modeling. This review covers recent approaches to make dosimetry more practical and easier to use in everyday RPT workflows. We introduce a theoretical framework in which simplification methods, which help ease the clinical burden of dosimetry, are classified into three related categories. The first two categories simplify the process through direct reduction in the burdens of either the acquisition or processing of data: (1) Methodological simplifications through reduction in the imaging burden, such as reduced- or single-time point (STP) imaging; and (2) Computational automation via artificial intelligence (AI) to automate dosimetry, including image processing, segmentation, registration, and AD estimation. Advanced modeling strategies in the third category improve efficiency through inference, thereby enabling estimation of AD from sparsely sampled data. Reduced imaging played a major role in defining these areas. While all of these have their promise, every category of methods has certain drawbacks. STP techniques are less reliable for heterogeneous tumors, black box models have poor transferability and explainability, and digital twin/physiologically-based pharmacokinetic (DT/PBPK) models lack clinical validation. Fundamental dosimetry, with appropriate validity conditions, could allow for reduced reliance on imaging skills and expertise while still retaining sufficient precision for its intended purposes. Standardization of imaging and multicenter analysis is essential for wider adoption of RPTs. A detailed description of models and their effectiveness would be helpful in the clinical application of dosimetry.