## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Treatment-Planning Systems  
In the field of multi-objective healthcare management applying ML-enabled ISs, treatment-planning systems serve as pillar technologies. From an ML point of view, these models apply complex algorithms to process and evaluate broad datasets, consisting of treatment outcomes, patient histories, and clinical situations. These systems are adapted to address various healthcare purposes, optimizing treatment techniques within standards like cost-effectiveness, clinical efficiency, and alignment with patient preferences. By controlling the abilities of ML, treatment-planning systems foster the transformation of accuracy in medicine, promoting healthcare professionals' data-enabled insights that improve decision-making processes. Ultimately, these models develop the total effectiveness and quality of healthcare services, seamlessly incorporating several purposes through a dynamic and complicated environment. [PERSON], [PERSON] [42] introduced a robust multi-objective method aimed at developing healthcare models' effectiveness during pandemics. The method incorporated location planning with improved optimization models to improve service delivery under unclear situations. By addressing several goals simultaneously, it guaranteed balanced trade-offs among availability, cost-efficiency, and system dependency. The paper focused on adaptability to dynamic pandemic scenarios and presented empirical insights for decision-making in healthcare practices. Furthermore, [PERSON], [PERSON] [20] proposed a multi-objective reinforcement learning (MORL) model for healthcare extension planning, especially during pandemic scenarios. The presented system optimized significant goals like cost-efficiency, resource allocation, and healthcare coverage by tailoring it to dynamic and uncertain situations. This method allows decision-makers to make informed, data-centric decisions to empower healthcare models' responsiveness and resilience in the face of pandemic events. Moreover, [PERSON], [PERSON] [43] proposed a multi-objective optimization system designed to support decision-making in sustainable environmental management. The method addressed competing goals, balancing economic effectiveness, ecological effect, and functional viability. It integrated developed computational models to optimize complicated systems under various limitations. The paper proposed empirical tools for decision-makers to achieve sustainable results. Additionally, [PERSON] and [PERSON] [44] offered a developed method called **\"Multi-Objective Deep Reinforcement Learning\" (MODRL)** for individualized dose optimization in healthcare. This method integrated a novel model termed **\"Multi-Indicator Experience Replay\"**, allowing the model to effectively learn and optimize dosing techniques by addressing several purposes concurrently. By utilizing the abilities of deep reinforcement learning, the model processed complicated datasets and experimental inputs, proposing a complex and highly personalized method for dose optimization. This methodology proposed a specific development in precision medicine, balancing several clinical objectives to improve treatment effectiveness and patient-specific results. In addition, [PERSON], [PERSON] [45] introduced a multi-objective optimization technique to address the complicated decision-making issues associated with hospital admissions, with an emphasized case study on COVID-19 patients. Their technique allows for simultaneous consideration of several goals, optimizing admission decisions by ensuring timely and appropriate patient care, minimizing patient wait times, and maximizing hospital resource utilization. The paper focused on the application of multi-objective optimization models in the improvement of healthcare operations, and Table 4 details the specifications of studies that examined treatment-planning systems within the context of multi-objective healthcare management.