## 5 Conclusions  
A precise understanding of dosimetric parameters and their toxicity correlations is essential for optimizing RT plans. This review underscores the importance of parameters like the DVH, Dmax, and Dmean, which are pivotal for predicting toxicity and tailoring treatments to PCa. Improved toxicity prediction enhances treatment efficacy and minimizes side effects, benefiting patients' QoL.  
Emerging technologies, particularly AI and ML, are expected to revolutionize toxicity prediction by analyzing extensive dosimetric and clinical data to identify complex patterns. The integration of advanced techniques such as IGRT and IMRT with AI offers a promising path towards more personalized and effective RT.  
The absence of public datasets in current studies underscores a significant need for standardized, large-scale datasets in the field. Standardization across datasets, including common protocols for data collection and uniformity in clinical annotations, would enhance the generalizability and reproducibility of research findings, making it easier to draw reliable conclusions across studies. This would also enable researchers to test and validate models on diverse patient populations, which is critical for ensuring model robustness, especially in personalized RT planning where individual differences greatly impact treatment outcomes.  
Large-scale, standardized datasets also facilitate the development of more accurate and unbiased AI models by reducing overfitting and ensuring that models are not tailored to a specific dataset, making them more adaptable to new, unseen data. The availability of such datasets could also accelerate research in the field, as researchers would spend less time gathering and cleaning data and more time on actual model development and validation.  
This improvement could greatly benefit future research efforts, fostering collaboration across institutions by providing a common data foundation. Furthermore, open access to standardized data supports transparency and trust, allowing for peer review, which is essential for advancing AI credibility and utility in clinical settings. The creation of standardized datasets would not only enhance research quality but could also speed up the translation of AI-based models into real-world clinical practice.  
Future research should address gaps in data and explore innovative applications of AI in various RT modalities, aiming to further refine treatment strategies and optimize patient outcomes while navigating ethical and technical challenges.  
Conceptualization, A.P. and A.C.; methodology, A.P. and A.C.; investigation, A.P., R.C., V.B., M.A. and A.C.; writing--original draft preparation, A.P.; writing--review and editing, A.P., R.C., V.B., M.A., R.L., P.A., G.S. and A.C.; visualization, A.P., R.C. V.B., M.A., R.L., P.A., A.D., D.C., L.B., G.S., D.D.R., A.T. and A.C.; supervision, V.B., M.A., R.C., D.D.R., A.T. and A.C.; project administration, A.C.; funding acquisition, A.C. and P.A. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the following projects: \"RADIATIONS\"--under the PNRR--Next Generation EU, Mission 4, Component 2--Investment 1.5--Cascading Call--Ecosystem of Innovation \"THE--Tuscany Health Ecosystem\"--code ECS0000017--CUP B83C22003930001, to [PERSON]; \"INNOVA\"--Advanced Diagnostic, Project promoted by Italian Ministry of Health--PNC-E3-2022-23681266 PNCHTS-DA 1, CUP D73C22 OO2090001, to [PERSON] and [PERSON].  
R.C. is a member of the \"Gruppo Nazionale per l'Analisi Matematica, la Probabilita e le loro Applicazioni\" (GNAMPA-INdAM).  
The authors declare no conflicts of interest.  
Artificial intelligence  
Artea Under the ROC Curve  
Brachytherapy  
Common Terminology Criteria for Adverse Events