## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Diagnostic Systems  
In the area of diagnostic systems across multi-objective healthcare management, the incorporation of AI and ML has transformed the diagnostic process by increasing accuracy, speed, and effectiveness. Particularly, ML-driven diagnostic systems use developed  
Figure 18: The average AUC-ROC of the studied paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**System Type** & **Accuracy (\%)** & **Efficiency (\%)** & **Adaptability (\%)** \\ \hline
**Diagnostic Systems** & 93 & 92 & 88 \\ \hline
**Treatment-Planning Systems** & 95 & 94 & 90 \\ \hline
**Resource Allocation Systems** & 90 & 91 & 85 \\ \hline
**Hybrid Systems** & 97 & 96 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of healthcare system types. ML algorithms to evaluate extensive and intricate datasets, consisting of clinical images, EHR, and diagnostic test outcomes. This allows for the detection of subtle paradigms, interrelations, and abnormalities that cannot be simply evident to human providers. For example, in radiology, ML algorithms may identify minute variations in imaging data that represent the early onset of diseases like cancer, facilitating earlier and more accurate diagnoses. Furthermore, these systems allow for individualized treatment plans by integrating diagnostic data with historical patient results, therefore developing patient management and resource allocation. However, the arrangement of ML in diagnostic systems is not without its issues [65]. The efficiency of these models heavily relies on the quality and variation of the training datasets, as insufficient or biased data can impair model function. Interpretability is a main concern, as the complication of ML algorithms usually obscures their decision-making procedures, potentially undermining reliability through healthcare practitioners. In addition, ensuring robust data privacy and compliance with regulations is principal to preserve sensitive patient data. Addressing these issues is critical for optimizing the advantages of ML in diagnostic systems and ensuring their dependable incorporation into multi-objective healthcare decision-making models. Even so, diagnosis systems, as critical elements of healthcare management for ML-driven IS, leverage ML methodologies to change the diagnosis process. These models apply strong algorithms and data analytics to explore large datasets, enabling precise and fast disease diagnosis. ML techniques might detect trends, paradigms, and abnormalities in patient data, leading to more precise and appropriate detections. Diagnostic technologies that employ ML in healthcare generate multiple benefits. They enable early disease diagnosis, timely intervention, and treatment planning. Moreover, these technologies enhance the efficiency of healthcare practitioners by proposing data-driven visions, which propel them to better patient results and resource management. The incorporation of ML in detection models specifies a transformative leap in healthcare management, presenting increased accuracy and efficiency in the diagnostic stage [66]. Diagnostic systems, performing through HIS for multi-objective decision-making and applying ML techniques, serve a key role in increasing clinical diagnostics. These models use complex algorithms to evaluate a variety of healthcare data, including medical images, patient records, and diagnostic test results. ML algorithms may detect complex patterns, connections, and abnormalities in these datasets, enabling early and accurate disease diagnosis. The use of diagnostic models is broad, ranging from the recognition of special medical situations in radiology images to the interpretation of complicated genetic data. The incorporation of ML increases the effectiveness, accuracy, and speed of diagnostic procedures, propelling more accurate and timely medical interventions. In addition, these models enable the optimization of resource usage across healthcare by streamlining diagnostic workflows and decreasing the likelihood of diagnostic faults.