{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:02:06Z","timestamp":1792789326264,"version":"4.5.0"},"reference-count":135,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hallym University Research Fund","id":[{"id":"https:\/\/ror.org\/03sbhge02","id-type":"ROR","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/03sbhge02","id-type":"ROR","asserted-by":"publisher"}]},{"name":"BIO&MEDICAL TECHNOLOGY DEVELOPMENT PROGRAM OF THE NATIONAL RESEARCH FOUNDATION (NRF) FUNDED BY THE KOREAN GOVERNMENT","award":["RS-2023-00223501"],"award-info":[{"award-number":["RS-2023-00223501"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Perioperative pulmonary and respiratory complications (PPRCs) result in increased morbidity, mortality, and healthcare costs. Recent research has focused on employing artificial intelligence (AI) and machine learning to predict and analyze PPRCs using expansive perioperative datasets. Despite advances in feature engineering, deep learning architectures, and explainable AI, models are rarely implemented in routine clinical practice. In this narrative review, we synthesize evidence from 59 unique publications, corresponding to 63 phenotype-specific study-listings across six PPRC phenotypes (combined postoperative pulmonary complications, pneumonia\/pulmonary infection, hypoxemia, respiratory failure, pulmonary embolism\/venous thromboembolism, and pulmonary edema) and introduce an integrated analytical and translational framework to bridge translation gap. The proposed paradigm comprises a tiered framework for evaluating outcome ascertainment quality (TIER) (39 of 63 study-listings [61.9%] were classified as TIER A according to the proposed framework), an algorithm\u2013cohort\u2013phenotype fit model, a discrimination\u2013calibration\u2013utility triad for performance assessment, a translation gap framework highlighting interacting challenges across validation, deployment, and evidence generation, and a six-step framework for clinical translation. Most models lacked robust validation, with only 9 of the 59 unique publications (15.3%) reporting true cross-institutional external validation, thereby limiting their generalizability. Near-term clinical translation is more likely through institution-specific development and local adaptation than direct widespread deployment, supported by concrete recommendations for clinicians.<\/jats:p>","DOI":"10.3390\/diagnostics16193082","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T10:17:26Z","timestamp":1790158646000},"page":"3082","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging the Artificial Intelligence Translation Gap in Perioperative Pulmonary and Respiratory Complications: A Practical Framework for Institution-Level Implementation"],"prefix":"10.3390","volume":"16","author":[{"given":"Ji-Yeon","family":"Lee","sequence":"first","affiliation":[{"name":"Big Data Center, Chuncheon Sacred Heart Hospital, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3121-1528","authenticated-orcid":false,"given":"Yong-Ho","family":"In","sequence":"additional","affiliation":[{"name":"Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3818-2239","authenticated-orcid":false,"given":"Jong-Ho","family":"Kim","sequence":"additional","affiliation":[{"name":"Big Data Center, Chuncheon Sacred Heart Hospital, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"},{"name":"Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"},{"name":"Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6035-786X","authenticated-orcid":false,"given":"Sung-Mi","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Jun","family":"Lee","sequence":"additional","affiliation":[{"name":"Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"},{"name":"Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7753-8464","authenticated-orcid":false,"given":"Young-Suk","family":"Kwon","sequence":"additional","affiliation":[{"name":"Big Data Center, Chuncheon Sacred Heart Hospital, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"},{"name":"Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"},{"name":"Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.4174\/astr.2016.90.1.21","article-title":"Impact of postoperative complications on clinical and economic consequences in pancreatic surgery","volume":"90","author":"Jon","year":"2016","journal-title":"Ann. 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