# Application of Artificial Intelligence in Support of NAFLD Diagnosis
## 5 Conclusions  
A comprehensive system for automated medical data analysis and patient diagnosis using artificial intelligence was developed as part of this study. The system consists of several vital modules, designed and implemented as scripts compatible with Azure Data Factory and Apache Airflow and conforming to Continuous Integration/Continuous Deployment (CICD) practices. The first module is responsible for aggregating medical data into a database. It makes it possible to collect and process large amounts of data from various sources, laying the foundation for subsequent analysis and training of AI models. The second module is designed to train artificial intelligence models. Advanced machine learning algorithms were used here and optimized for diagnostic accuracy. The third module, responsible for Explainable AI (XAI), provides an explanatory analysis of AI models' performance and report generation. The system generates two types of reports: a general report on the performance of the system and an individual patient diagnosis report that explains on what basis the model made a particular diagnostic decision. The system also has functionality that directly involves diagnosing patients based on the collected data. The baseline models that have been trained have achieved a quality of more than 90%, indicating their high diagnostic accuracy. The ensemble model, which combines results from several base models, achieved an impressive accuracy of 89% on the test set. Despite the system's high performance, further improvements are planned in the future by aggregating more data. The more extensive database will allow for more comprehensive post-training of models, contributing to even higher diagnostic accuracy and better adaptation of the system to various clinical cases. The system has been designed to automatically integrate new data into the model-training process, ensuring continuous improvement and updating [12; 13; 14; 15]. [PERSON]'s feature validity and value analyses have identified essential attributes that can be disease biomarkers. These findings have critical applications in clinical practice, improving the accuracy of diagnoses and supporting physicians' decision-making process. Continued research in this direction and validation of these results in more extensive clinical trials are crucial to fully realizing these findings [16]. Detected disease biomarkers have broad applications beyond automatic diagnosis. They can be used to monitor the progression of a disease, allowing an assessment of how a patient is responding to treatment and whether there is improvement, stabilization, or deterioration. This allows doctors to better adjust therapies, providing more personalized treatment. Biomarkers can also be used to predict disease risk, helping to identify those at risk for a particular disease even before symptoms appear, allowing preventive strategies or early intervention to be introduced. Another important application is the use of biomarkers in clinical trials, where they are a tool for evaluating the effectiveness of new therapies or drugs. They can provide objective data on the body's response to treatment, which reduces trial time and costs, and allows new therapies to be brought to market more quickly. In conclusion, the created system is a modern tool to support medical diagnosis using advanced artificial intelligence technologies. Its modular design and compatibility with modern data and process management tools ensure flexibility and scalability, which is vital to its further development and application in clinical practice. Conceptualization, J.M. ; Software, J.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted using fully anonymized data provided by the hospital. According to the hospital's data-sharing policies and relevant legal regulations, no formal approval by an Institutional Review Board (IRB) was required. Not applicable.