## 2 Background
### Skin Pathologies
Automated detection systems play a crucial role in advancing the prognosis of skin cancer by identifying suspicious lesions early on, allowing for timely interventions and ultimately improving patient outcomes. The impact extends to a reduction in mortality risks associated with skin cancer, particularly as early detection prevents the disease from progressing to critical stages that may affect vital organs. Moreover, the use of automated systems offers significant cost savings in treatment, as it enables less invasive and more cost-effective alternatives for addressing skin cancer, benefiting both patients and healthcare systems. The integration of these systems into routine health check-ups promotes regular screenings, fostering awareness about monitoring moles and other skin irregularities. Targeted screening for high-risk populations, such as those with a family history of skin cancer or excessive sun exposure, is facilitated by automated detection systems, leading to the early identification of suspicious lesions. The scalability and efficiency of these systems enable swift and accurate analysis of a large volume of images, making them ideal for screening extensive populations and thereby enhancing the chances of early skin cancer detection. Figure 3: Basal Cell Carcinoma (**a**), Melanoma (**b**), Seborrheic keratosis (**c**), Actinic keratosis (**d**). Healthcare professionals also benefit from the assistance provided by automated detection systems, as they aid in identifying potential melanoma cases, improving diagnostic accuracy, and reducing the likelihood of overlooking concerning lesions. Ultimately, patient empowerment is achieved through prompt feedback from automated systems, enabling individuals to take proactive measures for additional assessments and early treatment when necessary. The proposed solution brings additional benefits from the perspective of having an automated pathology detection system within an EHR system that consolidates all workflows for medical team members in one place:  
* Improved patient outcomes: Early detection of pathologies allows for timely interventions and treatments, significantly improving patient outcomes. Skin and eye pathologies, if left undetected and untreated, can progress and lead to more severe health issues. By detecting these conditions in their early stages, healthcare professionals can implement effective treatment plans and prevent complications. * Disease progression prevention: Pathologies can progress rapidly if not identified in a timely manner. Integrating automated detection systems into an EHR allows healthcare providers to monitor patients continuously and detect any changes in their health status. This proactive approach helps prevent disease progression, reducing the risk of long-term deterioration and potentially saving lives. * Reduced healthcare costs: early detection and intervention can lead to less complex and invasive treatments, generally reducing healthcare costs by minimizing hospitalizations and resulting in significant savings for both patients and healthcare systems. * Increased access: An automated detection system integrated into an EHR provides patients with access to real-time medical information. This promotes patient engagement and encourages patients to play an active role in managing their health, making informed decisions, and adhering to treatment plans. * Population health management: EHR systems with integrated detection capabilities enable healthcare providers to analyze trends and patterns within patient populations. This data-driven approach allows public health authorities to identify high-risk groups, implement targeted preventive measures, and allocate resources efficiently. * Streamlined workflow: Integrating automated detection into EHR systems streamlines healthcare workflows. Clinicians can access patients' medical history, investigation results, and treatment plans in a centralized platform, facilitating quick decision making and reducing the risk of omissions or communication errors.