## 6 Ethical Correlates and Limitations
We took into consideration both accuracy and F1 score to ensure that the system is both reliable and accurate. In the subsequent phase of our research, implementing this prototype within a medical institution entails addressing social and psychological factors. This encompasses both educating and providing support to patients throughout the process. * Psychological Impact: Early detection of potentially harmful pathologies through automated screening may have psychological implications for patients. This includes managing anxiety, coping with uncertainty, and providing appropriate support for individuals undergoing further diagnostic procedures. * Patient Autonomy: While automated screening systems may improve early detection rates, patients should retain autonomy in healthcare decision-making. Respect for patient preferences and values should guide discussions about screening, diagnosis, and treatment. * Trust in Healthcare Providers: The introduction of automated screening systems may impact patient-provider relationships and trust. Open communication, shared decision-making, and transparency about the limitations of technology can help maintain trust and confidence in healthcare providers. * Societal Impact: Implementation of automated screening systems may have broader societal implications, including changes in healthcare delivery, resource allocation, and health policy. Ethical considerations should address potential societal benefits and risks associated with widespread adoption of these technologies. Introducing a digital system, particularly a medical information system, into a healthcare institution involves recognizing and addressing limitations early in the project, with a focus on finding solutions to overcome these challenges. The limitations of the proposed system are as follows:  
* Limited Scope: Automated classification modules may be limited in their scope and may not cover all possible diagnoses or conditions. Certain rare or complex conditions may not be adequately captured by the system, requiring manual intervention by healthcare providers. * User Adoption and Training: The success of an information system depends on user adoption and proficiency. If users are not adequately trained or if the system is not user-friendly, it may lead to low adoption rates and underutilization of the system's capabilities. * Maintenance and Updates: Automated classification algorithms require regular maintenance and updates to remain effective and up to date with the latest medical knowledge and technological advancements. This ongoing maintenance can be resource-intensive and time-consuming. * Integration Challenges: Integrating an automated classification module into an existing EHR system can be complex and may require significant changes to the system's infrastructure and workflow. Ensuring seamless integration and compatibility with existing processes is crucial for successful implementation. * Interpretability: Deep learning algorithms used for automated classification often lack interpretability, making it challenging to understand why a particular classification decision was made. This lack of transparency can undermine trust in the system among healthcare providers and patients. * Dependence on Technology: Information systems rely on technology infrastructure, software, and networks. Any disruptions or failures in these components can impact the availability and functionality of the system. * Cost: Developing, implementing, and maintaining an information system can be expensive. Organizations need to budget for initial development costs as well as ongoing operational expenses, including licensing fees, hardware upgrades, and personnel costs. * Maintenance: Information systems require regular maintenance, updates, and patches to ensure optimal performance and security. Neglecting maintenance tasks can result in system downtime, vulnerabilities, and performance issues.