## 4 Discussion
The integration of advanced technologies, such as image-guided radiotherapy (IGRT) and intensity-modulated radiotherapy (IMRT), together with an in-depth understanding of dosimetric parameters, represents the future path to safer and more personalized RT treatment of PCa [79; 154]. Although public datasets are missing and there is a lack of method details in some studies that hamper the replicability and independent verification of results, the use of advanced software and robust statistical models, such as logistic regression analysis, contributes to the validity of results [62]. The topic of this review focused on RT with conventional fractionation, while many screened and rejected studies were aimed at ultra-fractionation and brachytherapy treatments, so it would be useful to perform reviews with these two topics to possibly find innovative applications of AI in finding predictive dosimetric parameters in such increasingly popular care. Applications of AI in the prediction of toxicity and outcomes of RT treatments represent a rapidly evolving area of medicine [155, 156, 157, 158]. RT practitioners may hesitate to adopt AI models that operate as \"black boxes\", as this lack of transparency can undermine trust in AI-driven decisions. To build radiation oncologists' trust, it is essential to promote transparency by explaining how AI models work, using feature importance and visualization tools. Additionally, clarifying that AI is a supportive tool, with final decisions always resting with the physician, reinforces their role in patient care. Allowing oncologists to adjust potential AI errors not only enhances patient safety but also contributes to refining the AI model itself, improving its accuracy over time. Sharing case studies that showcase AI's positive impact and involving radiation oncologists in the development process further fosters understanding, while providing targeted training empowers practitioners to confidently integrate AI into their workflows [159, 160]. Future trends in advanced medical technology focus on large language models (LLMs) and digital twins, which hold transformative potential for healthcare. LLMs can enhance diagnostics, treatment personalization, and patient education, while digital twins--virtual models of patients--could enable real-time monitoring, predictive analyses, and tailored interventions, pushing precision medicine forward [161, 162, 163]. In the future, these applications could have a significant impact in several ways. AI has the capability to analyze large amounts of clinical data and medical images to identify patterns and predict how patients will respond to specific treatments. This analysis could enable physicians to tailor plans to each patient's individual characteristics, thereby improving treatment effectiveness and reducing toxicity. AI can help optimize RT doses, balancing treatment efficacy with toxicity risk. This can lead to better tumor control and reduced side effects [164, 165]. AI systems can be employed to monitor patients in real-time during RT delivery, facilitating the detection of changes in treatment response and allowing timely adjustments to the treatment plan [79]. In RT prediction, balancing model complexity and real-time performance is essential. While complex models like deep neural networks offer higher accuracy, they demand more computational resources and longer processing times, which can delay clinical decisions. Conversely, simpler models, such as logistic regression, provide faster results with adequate accuracy for specific applications [144]. Choosing the right model depends on the clinical context. For situations requiring rapid feedback, simpler models are often preferred, while complex models may be needed for more intricate data patterns. Ultimately, effective AI in RT must ensure both accuracy and computational efficiency, allowing clinicians to make timely decisions without compromising predictive power [166].