ABSTRACT
Artificial intelligence (AI) in healthcare can potentially improve patient outcomes, operational efficiency, and diagnostic accuracy. However, it also raises serious ethical issues, especially in light of possible disparities in the distribution and accessibility of AI-powered healthcare resources. This study investigates how AI might affect health disparities. It bases its proposal for an equitable AI implementation framework on the justice teachings of the Catholic Church. In line with the Church's ethical commitment to social justice, the paper makes an ethical case for a responsible approach to AI in healthcare by examining the concepts of human dignity, the common good, and preferential option for the poor.
ABSTRACT
AIMS: Diabetes Mellitus (DM) continues to burden millions of people worldwide. Early detection and effective diagnosis of DM are essential key strategies to reduce the impeding incidence of the disease and its complications. Thus, this study determined the potential utility of salivary glucose, amylase, calcium, and phosphorus as non-invasive diagnostic markers of DM. MATERIALS AND METHODS: A total of 80 participants were recruited and divided into two groups (non-diabetics and diabetics). Fasting blood samples and unstimulated saliva samples were collected and tested for glucose, amylase, calcium, and phosphorus. RESULTS: Mann-Whitney U test shows that salivary glucose and salivary amylase were significantly higher among diabetics than non-diabetics. In addition to this, the receiver operations characteristics (ROC) curve showed that salivary glucose (AUCâ¯=â¯0.811, pâ¯<â¯0.001) and amylase (AUCâ¯=â¯0.649, pâ¯=â¯0.03) has significant association with DM. CONCLUSION: Overall, only salivary glucose and amylase showed good potential in discriminating patients with diabetes from those without.