## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Treatment-Planning Systems  
In this context, AI serves a key role in increasing multi-objective decision-making by optimizing treatment techniques for diverse healthcare situations [69]. AI-driven treatment-planning systems use complex algorithms to evaluate patient data, medical guidelines, and historical treatment results to provide individualized treatment plans that balance several purposes, like effectiveness, security, cost, and patient preferences. For instance, in oncology, AI methods could recommend radiation doses that maximize tumor control while minimizing failure to enclose healthy tissues, as a result attaining an optimum balance between treatment efficiency and patient quality of life. Moreover, these models allow for the real-time deployment of treatment plans according to patient responses and emerging data, ensuring that care remains dynamic and responsive to variations in the patient's situation. However, the incorporation of AI in treatment planning also proposes alternation, especially in ensuring the interpretability of AI-provided recommendations [70]. The complication of AI algorithms could make it difficult for healthcare practitioners to understand the logic behind significant treatment suggestions, which might impede their acceptance and implementation. In addition, the dependence upon broad and various datasets for training AI models boosts concerns about data quality and representativeness, which are significant for accurate and equitable treatment planning through various patient populations. Addressing these issues is fundamental for fully controlling the potential of AI in treatment-planning systems, therefore developing patient results and optimizing resource allocation in healthcare. In addition, treatment-planning systems, integral elements of IS for multi-objective healthcare management, control ML methods that optimize the formulation of holistic treatment methods. These systems use complex algorithms to evaluate patient-specific data, containing medical histories, genetic data, and treatment responses. ML algorithms allow treatment-planning systems to detect individualized and efficient treatment choices by taking into account a number of variables and forecasting potential findings. The advantages of integrating ML into treatment planning are important, ranging from enhanced treatment effectiveness and dropped side effects to better resource application. By applying treatment plans to personal patient features and forecasting responses, these models promote the progress of accurate medicine, therefore improving patient care and findings in the complicated vision of healthcare management [71]. Moreover, treatment-planning systems, as integral components of ISs for multi-objective healthcare management using MLmethods, represent a variety of applications that considerably increase medical decision-making. These models apply ML algorithms to evaluate patient-specific data, containing medical histories, diagnostic outcomes, and genetic data, to formulate individualized and optimized treatment plans. By using predictive modeling, these models allow healthcare practitioners to select interventions, forecast potential treatment findings, and detect the most efficient therapeutic methods for personal patients. The incorporation of ML in treatment-planning systems promotes the detection of trends and connections through broad datasets, propelling more accurate and data-driven treatment recommendations. This technology holds promise in developing treatment effectiveness, lowering adverse impacts, and lastly, enhancing the quality of patient care in multi-objective healthcare management scenarios. In spite of the notable benefits, treatment-planning systems, such as ISs leveraging ML in multi-objective healthcare management, confront given drawbacks and issues. One important issue is the dependence on the quality and completeness of the input data, as incorrect or missing patient data may endanger the precision of treatment suggestions [72].