{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T11:27:38Z","timestamp":1771932458556,"version":"3.50.1"},"reference-count":2,"publisher":"American Academy of Insurance Medicine","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Insur Med"],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:sec>\n                    <jats:title>Background.\u2014<\/jats:title>\n                    <jats:p>Cardiovascular risk estimation for life insurance underwriting relies on risk estimation from conventional metrics: age, sex, smoking status, body mass index, systolic and diastolic blood pressure, total and high-density lipoprotein cholesterol and stress electrocardiogram. Coronary artery calcium (CAC) scoring via CT is a validated predictor of cardiovascular risk but remains costly, invasive, and unsuitable for large-scale underwriting. A novel artificial intelligence (AI) model, RetiCAC, predicts CAC scores from retinal photographs, offering a non-invasive and scalable alternative.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective.\u2014<\/jats:title>\n                    <jats:p>To assess the potential role of RetiCAC in life insurance underwriting for improved cardiovascular risk stratification and pricing accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods.\u2014<\/jats:title>\n                    <jats:p>This review draws on evidence from The Lancet Digital Health study of RetiCAC and evaluates its accuracy to and prognostic value compared with traditional CAC scoring. Potential underwriting applications were considered, including risk stratification, replacement of costly diagnostics, predictive augmentation, improvement in customer experience and integration with dynamic underwriting models and wellness programs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results.\u2014<\/jats:title>\n                    <jats:p>RetiCAC demonstrated incremental predictive prognostic value, particularly in borderline and intermediate-risk groups, and showed comparable performance to CT-derived CAC scoring in external cohorts. For insurers, RetiCAC could enable scalable, non-invasive cardiovascular risk assessment, refine mortality predictions, and improve classification of substandard applicants. Its digital nature supports remote underwriting models and wellness integration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion.\u2014<\/jats:title>\n                    <jats:p>RetiCAC has potential as a non-invasive adjunct to traditional underwriting, enhancing cardiovascular risk prediction while reducing reliance on invasive testing. Broader adoption will require further validation, regulatory approval, and ethical safeguards, but integration could provide insurers with competitive advantages and align risk assessment with preventive health strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.17849\/insm-53-1-1-3.2","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:51:37Z","timestamp":1770771097000},"page":"78-80","source":"Crossref","is-referenced-by-count":0,"title":["Retinal Coronary Artery Calcium Scoring \u2013 A Scalable Tool for Life Insurers"],"prefix":"10.17849","volume":"53","author":[{"given":"Muhammed Adil","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7324","reference":[{"key":"i-53-1-78-ref01","series-title":"Lancet Digit Health","first-page":"e306","article-title":"Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs","volume":"3","author":"Rim","year":"2021","unstructured":"Rim TH,\nLee CJ,\nTham YC, et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health. 2021;3:e306\u2013e316. doi:10.1016\/S2589-7500(21)00043-1"},{"key":"i-53-1-78-ref01","series-title":"Lancet Digit Health","first-page":"e306","article-title":"Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs","volume":"3","author":"Rim","year":"2021","unstructured":"Rim TH,\nLee CJ,\nTham YC, et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health. 2021;3:e306\u2013e316. doi:10.1016\/S2589-7500(21)00043-1"}],"container-title":["Journal of Insurance Medicine"],"original-title":[],"link":[{"URL":"https:\/\/jim.kglmeridian.com\/view\/journals\/insm\/53\/1\/article-p78.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jim.kglmeridian.com\/downloadpdf\/journals\/insm\/53\/1\/article-p78.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/jim.kglmeridian.com\/downloadpdf\/journals\/insm\/53\/1\/article-p78.xml","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T10:46:56Z","timestamp":1771930016000},"score":1,"resource":{"primary":{"URL":"https:\/\/jim.kglmeridian.com\/view\/journals\/insm\/53\/1\/article-p78.xml"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":2,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.17849\/insm-53-1-1-3.2","relation":{},"ISSN":["0743-6661"],"issn-type":[{"value":"0743-6661","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]}}}