## 8 Conclusions  
In summary, this article's findings and contributions offer fresh perspectives on healthcare technology through the introduction of a medical information system developed within the SF environment. The standout feature of this system is its automated classification module, which addresses both ocular and skin pathologies. By harnessing the capabilities of GT, our integration combines state-of-the-art technology with the robust functionalities of SF, offering a comprehensive solution tailored for medical practitioners. The article meticulously explores the intricate structure of the system, shedding light on its innovative functionalities meticulously crafted to augment diagnostic precision and streamline medical workflows. The methods employed in the development of these functionalities are dissected, offering valuable insights into the seamless integration of GT within the SF framework. This collaborative approach not only propels the landscape of medical information systems forward but also signifies a momentous stride towards intelligent and efficient pathology classification. The article's contributions extend beyond the technological intricacies, emphasizing the system's ability to classify diverse pathologies, including ocular complications of diabetes (cataract, diabetic retinopathy, glaucoma), skin cancers like melanoma and basal cell carcinoma, and other potential precancerous conditions such as actinic keratosis and seborrheic keratosis. By addressing these specific pathologies, the system stands poised to significantly impact the healthcare domain, offering a comprehensive and intelligent solution for practitioners. In essence, this article marks a transformative chapter in medical information systems, blending advanced technology with practical applications to elevate the standards of pathology classification. The amalgamation of GT and SF not only presents an innovative solution for current medical challenges but also sets a precedent for future developments in intelligent healthcare systems. The integration of the automated detection system into an EHR as a concept holds promise for facilitating more frequent assessments by including screenings during patient visits, thereby potentially enhancing the chance of early detection of suspicious changes. This would also translate into numerous advantages for healthcare providers. The automated detection system streamlines the screening process, enabling healthcare professionals to analyze images efficiently and flag potentially concerning cases for further examination. Conceptually, this approach alleviates the workload of healthcare providers, enabling them to concentrate on crucial aspects of patient care. It positions this implementation as a support tool for clinical personnel. The system provides real-time alerts, promptly notifying healthcare professionals of suspicious changes during routine visits or medical record reviews, facilitating timely monitoring and intervention. Utilizing advanced algorithms and machine learning, the automated detection system enhances diagnostic accuracy, contributing to the reduction in human errors (like exhaustion, misinterpretation of symptoms, incomplete examination, diagnostic bias, limited experience, diagnostic uncertainty and so on). Patient engagement and education are fostered through EHR systems, allowing patients to comprehend screening results, understand risk factors, and take a proactive approach to their health. High-risk patients benefit from efficient monitoring, as the integrated system allows healthcare providers to track them more effectively, ensuring regular screenings and follow-up evaluations. The accumulation of data from multiple screenings and patient histories enables data-driven decision-making and trend analysis, leading to targeted prevention and intervention strategies.