{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T02:52:55Z","timestamp":1777949575890,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:00:00Z","timestamp":1777680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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 (Ee) 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, Ee links transparency with governance-ready evidence of trustworthiness for human\u2013AI collaboration.<\/jats:p>","DOI":"10.3390\/info17050438","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T01:12:09Z","timestamp":1777857129000},"page":"438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Defining an Ethical Explainability Metric for Measuring AI Trustworthiness in Connected Healthcare Systems"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3497-8838","authenticated-orcid":false,"given":"Parul","family":"Naib","sequence":"first","affiliation":[{"name":"Centre for Decision Support Systems, School of Global Health Management and Informatics, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9586-5570","authenticated-orcid":false,"given":"Jaeyoung","family":"Park","sequence":"additional","affiliation":[{"name":"Centre for Decision Support Systems, School of Global Health Management and Informatics, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paniz","family":"Abedin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1157-5734","authenticated-orcid":false,"given":"Christian","family":"King","sequence":"additional","affiliation":[{"name":"Centre for Decision Support Systems, School of Global Health Management and Informatics, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5723-7998","authenticated-orcid":false,"given":"Varadraj","family":"Gurupur","sequence":"additional","affiliation":[{"name":"Centre for Decision Support Systems, School of Global Health Management and Informatics, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,2]]},"reference":[{"key":"ref_1","unstructured":"Stanford University (2025, October 11). 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