{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:43:28Z","timestamp":1792806208831,"version":"4.5.0"},"reference-count":23,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>\n                    This narrative review explores the evolving role of artificial intelligence (AI) in haemodynamic monitoring, emphasising its potential to revolutionise patient care. The historical reliance on invasive procedures for haemodynamic assessments is contrasted with the emerging non-invasive AI-driven approaches that address limitations and risks associated with traditional methods. Developing the hypotension prediction index and introducing CircEWS\n                    <jats:sup>TM<\/jats:sup>\n                    and CircEWS-lite\n                    <jats:sup>TM<\/jats:sup>\n                    showcase AI\u2019s effectiveness in predicting and managing circulatory failure. The crucial aspects include the balance between AI and healthcare professionals, ethical considerations, and the need for regulatory frameworks. The use of AI in haemodynamic monitoring will keep growing with ongoing research, better technology, and teamwork. As we navigate these advancements, it is crucial to balance AI\u2019s power and healthcare professionals\u2019 essential role. Clinicians must continue to use their clinical acumen to ensure that patient outliers or system problems do not compromise the treatment of the condition and patient safety.\n                  <\/jats:p>","DOI":"10.4103\/ija.ija_1260_23","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T18:05:36Z","timestamp":1705514736000},"page":"93-99","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":16,"title":["Role of artificial intelligence in haemodynamic monitoring"],"prefix":"10.4103","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6761-163X","authenticated-orcid":false,"given":"Sheila N.","family":"Myatra","sequence":"first","affiliation":[{"name":"Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3068-1201","authenticated-orcid":false,"given":"Bharat G.","family":"Jagiasi","sequence":"additional","affiliation":[{"name":"Director of Critical Care Department, Kokilaben Dhirubhai Ambani Hospital, Navi Mumbai, Maharashtra, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1617-7735","authenticated-orcid":false,"given":"Neeraj P.","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7384-4886","authenticated-orcid":false,"given":"Jigeeshu V.","family":"Divatia","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi 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