## 1 Introduction  
Prostate cancer (PCa) [1; 2; 3; 4; 5; 6] is the second most common cancer among men, with a significant impact on quality of life and mortality. Radiotherapy (RT) is one of the primary therapeutic options [7; 8; 9; 10; 11; 12], offering significant benefits in local tumor control and survival [13; 14; 15]. Recent advances in medical imaging have significantly improved the accuracy and effectiveness of RT within the framework of precision medicine, making RT the most commonly used locoregional conventional cancer treatment [16; 17; 18; 19; 20; 21]. Despite these advancements, toxicity-related side effects are a major concern for both patients and healthcare providers [22; 23; 24; 25; 26; 27]. Dosimetric parameters play a pivotal role in predicting RT toxicity [28; 29; 30; 31; 32; 33; 34], as they determine the distribution of radiation dose within the target tissue and surrounding healthy tissue [35; 36]. In order to maximize treatment efficacy while minimizing side effects, it is essential to accurately predict these parameters. A key component of recent RT progress has been the integration of advanced imaging techniques, optimization algorithms, and predictive models based on AI [37; 38; 39; 40; 41; 42]. These innovations have transformed the field of biomedical analysis, enhancing segmentation [43; 44; 45; 46; 47; 48; 49; 50; 51; 52], defining predictive models [53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64], and improving radiopharmaceutical distribution [65; 66; 67; 68; 69; 70] and RT dosimetry [71; 72; 73; 74]. These tools enable highly personalized treatment plans by adapting the dose distribution to the unique characteristics of each patient and their specific tumor [75; 76; 77; 78; 79]. However, the potential for toxicity ranging from acute to chronic effects remains a significant aspect of RT, often affecting patients' quality of life (QoL) [80; 81; 82]. Acute toxicity, occurring during or shortly after treatment, often involves urinary and gastrointestinal symptoms such as frequent urination, pain, and diarrhea, but these symptoms usually subside within weeks to months [80; 83; 84; 85; 86]. In contrast, late toxicity, which can develop months to years later, may cause more persistent problems, including urinary incontinence, chronic bladder irritation, bowel dysfunction, and erectile dysfunction [87; 88; 89]. The early identification and prediction of RT-induced toxicity is therefore crucial to optimize treatment protocols and improve patients' QoL [90; 91; 92; 93; 94]. Smart healthcare, AI, and machine learning (ML) have transformed the medical field, particularly in cancer diagnosis and treatment. Historically, PCa management has progressed from basic screening methods in the early 20 th century to advanced imaging and targeted treatments today. With the introduction of the prostate-specific antigen (PSA) test in the 1980s, early detection improved, although treatment options were still limited to surgery and traditional RT [95; 96]. By the late 1990s, advances in RT allowed for more precise dose targeting, minimizing damage to surrounding tissue. The rise of AI and ML in the 2010s began a new era of predictive models, aiming to enhance patient outcomes by forecasting disease progression and personalizing treatment. Predictive RT, in particular, benefits from ML models trained on extensive datasets, enabling precise dose-volume histogram (DVH) predictions, toxicity outcomes, and personalized treatment adjustments [75]. In modern smart healthcare, wearable technology and AI-powered monitoring systems provide real-time patient feedback, making it possible to continuously refine treatments. As AI continues to develop, PCa care increasingly shifts toward precision medicine, leveraging complex algorithms to assess individual risk factors and optimize treatment plans, promising improved patient-specific outcomes in the near future. In recent years, research has focused on identifying predictors of RT toxicity, including clinical, dosimetric, and biological variables.