Leveraging Artificial Intelligence (AI) ethically in connected healthcare systems requires a quantifiable framework that measures not only outcome correctness, but also the clarity, auditability, and ethical acceptability of model explanations in high-stakes clinical and cybersecurity workflows. This manuscript first presents a narrative review of ethical risks and countermeasures in Healthcare Internet of Things (HIoT) and explains why existing performance metrics are insufficient for trustworthy deployment. We then formalize a quantitative metric called Ethical Explainability (E_e) as a composite index integrating (1) a Human Agreement Ratio (HAR), capturing concordance between AI recommendations (and their rationale) and a calibrated expert consensus, and (2) an Entropy Reduction Index (ERI), capturing the proportional reduction in expert uncertainty after receiving an explanation, operationalized via probability-elicitation questionnaires mapped to Shannon entropy. Designed for HIoT security monitoring, E_e links transparency with governance-ready evidence of trustworthiness for human–AI collaboration.

Background: Diabetes is associated with an increased risk of oral health complications, yet adults with diabetes remain less likely to obtain dental care in the United States. Objective: To examine socioeconomic, demographic, and insurance-related determinants of dental spending among adults with and without diabetes using nationally representative 2023 Medical Expenditure Panel Survey (MEPS) data. Materials and Methods: A two-part model was estimated: (1) a logistic regression predicting the likelihood of any dental spending, and (2) a generalized linear model (GLM) with a Gamma distribution and log link for positive spending. Covariates included age, sex, race/ethnicity, education, income, region, and insurance coverage. Results: The MEPS dataset included 15,071 adults (diabetes n = 2104; no diabetes n = 12,967), Adults with diabetes were less likely to have any dental spending than adults without diabetes (38.6% vs. 43.8%). Mean expenditures among users were higher for adults with diabetes but diabetes was not a statistically significant predictor of conditional spending after adjustment. Insurance coverage and preventive care were strong positive predictors of dental spending. Conclusions: Insurance coverage and preventive dental care are major drivers of dental spending and utilization among U.S. adults. Disparities observed among adults with diabetes appear to be driven primarily by reduced access to dental care rather than differences in spending intensity once care is obtained.