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Adaptive radiotherapy has recently drawn benefit from the introduction into the market of multiple certified hybrid linacs, which installations and number of treated fractions is steadily increasing. The main aim of treatment plan adaption is to compensate for the anatomical changes. Currently clinically implemented systems based on CBCT or MRI for image guidance are designed to re-optimize and update a base plan taking into account the inter-fractional changes, which are assessed during pre-treatment imaging. Intra-fractional motion is currently mitigated through gating, while volume tracking and additional techniques are still under development and in the research phase. In particular, MR guided radiotherapy (MRgRT) exploits the superior soft tissue contrast and the absence of imaging dose, which allows continuous imaging during beam delivery for monitoring intra-faction motion. The integration of such sophisticated imaging modalities with the linacs and the on-table adaptive workflows pose new challenges for medical physicists. The former requires dedicated procedures especially for hybrid MR-linacs, where the presence of the magnetic field does not allow the employment of conventional quality assurance (QA) phantoms and detectors. MR-compatible devices have to be developed and integrated in the routine QA program. Moreover, the effect of the magnetic field on the dose distribution and in the response of the detectors has to be quantified and taken into account. Additional challenges include the introduction of a QA program for the MR itself, which has not been historically part of radiation therapy departments. Special care should be dedicated to the evaluation of the geometrical distortion, which is critical for the accurate delivery of treatment plans in MRgRT. Even more challenging are the procedures related to the on-table adaptive workflows. Optimizing and limiting the patient on-couch time is of utmost importance for an even wider employment of adaptive radiotherapy. Therefore, automation and artificial intelligence (AI) are already playing and will play an always larger role within the clinical workflows. Automatic contouring and automatic re-planning are solutions that should aid clinicians and physicist in shortening and standardizing the treatments. The quantitative assessment of the quality of automatically generated contours and plans should also be included within the competences of medical physicist in adaptive radiotherapy. Furthermore, traditional workflows including pre-treatment patient specific QA (PSQA) cannot be applied in the case of adaptive radiotherapy when the new plan is generated and delivered while the patient is on-couch. Class solutions and independent verification algorithms should be implemented in the clinical workflow. Finally, adaptive radiotherapy is not only supporting developments aiming to automate human-based tasks (e.g. contouring, planning) but also driving towards the introduction of fully AI-based solutions (e.g. synthetic CT). In such cases, novel QA solutions have to be developed and clinically implemented, replacing the non-applicable traditional end-to-end testing. In conclusion, adaptive radiotherapy is generating increasing clinical evidence and is a stimulating and challenging field for new solutions to be developed and implemented by medical physicists.