{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T22:15:14Z","timestamp":1790288114666,"version":"4.1.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,25]]},"DOI":"10.7759\/cureus.97747","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T14:23:26Z","timestamp":1764080606000},"source":"Crossref","is-referenced-by-count":4,"title":["A Systematic Literature Review of Precision Anesthesia Through Machine Learning: Automated Drug Titration and Real-Time Physiologic Optimization"],"prefix":"10.7759","author":[{"given":"Rayan","family":"Zarei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leslie","family":"Torgerson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.47611\/jsrhs.v12i2.4403","article-title":"The application of artificial intelligence and machine learning to anesthesiology","volume":"12","author":"Appavu R","year":"2023","unstructured":"Appavu R. The application of artificial intelligence and machine learning to anesthesiology. J Stud Res. 2023, 12:14-6. 10.47611\/jsrhs.v12i2.4403","journal-title":"J Stud Res"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s44254-024-00085-z","article-title":"Advances in automated anesthesia: a comprehensive review","volume":"3","author":"Cai X","year":"2025","unstructured":"Cai X, Wang X, Zhu Y, Yao Y, Chen J. Advances in automated anesthesia: a comprehensive review. Anesthesiol Perioper Sci. 2025, 3:14-6. 10.1007\/s44254-024-00085-z","journal-title":"Anesthesiol Perioper Sci"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.18231\/j.ijca.2023.070","article-title":"The evolution of anesthesia: embracing emerging technologies and AI","volume":"10","author":"Gupta L","year":"2023","unstructured":"Gupta L. The evolution of anesthesia: embracing emerging technologies and AI. Indian J Clin Anaesth. 2023, 10:331-3. 10.18231\/j.ijca.2023.070","journal-title":"Indian J Clin Anaesth"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.21037\/atm-21-7031","article-title":"Artificial intelligence and anesthesia: a narrative review","volume":"10","author":"Bellini V","year":"2022","unstructured":"Bellini V, Rafano Carn\u00e0 E, Russo M, Di Vincenzo F, Berghenti M, Baciarello M, Bignami E. Artificial intelligence and anesthesia: a narrative review. Ann Transl Med. 2022, 10:528. 10.21037\/atm-21-7031","journal-title":"Ann Transl Med"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.5812\/jcma-145369","article-title":"Machine learning-guided anesthesiology: a review of recent advances and clinical applications","volume":"4","author":"Hashemi S","year":"2024","unstructured":"Hashemi S, Yousefzadeh Z, Abin AA, Ejmalian A, Nabavi S, Dabbagh A. Machine learning-guided anesthesiology: a review of recent advances and clinical applications. J Compr Med Adv Healthc. 2024, 4:14-6. 10.5812\/jcma-145369","journal-title":"J Compr Med Adv Healthc"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.anclin.2021.03.012","article-title":"Machine learning, deep learning, and closed loop devices-anesthesia delivery","volume":"39","author":"Wingert T","year":"2021","unstructured":"Wingert T, Lee C, Cannesson M. Machine learning, deep learning, and closed loop devices-anesthesia delivery. Anesthesiol Clin. 2021, 39:565-81. 10.1016\/j.anclin.2021.03.012","journal-title":"Anesthesiol Clin"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/access.2025.3605643","article-title":"An interpretable deep actor-critic framework for automated propofol dosing during general anesthesia","volume":"9","author":"Zhang D","year":"2025","unstructured":"Zhang D, Wang F. An interpretable deep actor-critic framework for automated propofol dosing during general anesthesia. IEEE Access. 2025, 9:16-8. 10.1109\/access.2025.3605643","journal-title":"IEEE Access"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3321099","article-title":"Towards real-world applications of personalized anesthesia using policy constraint Q learning for propofol infusion control","volume":"PP","author":"Cai X","year":"2023","unstructured":"Cai X, Chen J, Zhu Y, Wang B, Yao Y. Towards real-world applications of personalized anesthesia using policy constraint Q learning for propofol infusion control. IEEE J Biomed Health Inform. 2023, PP:32-4. 10.1109\/JBHI.2023.3321099","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref9","article-title":"Multi-agent deep reinforcement learning for multiple anesthetics collaborative control (PREPRINT)","volume":"6","author":"Li H","year":"2025","unstructured":"Li H, Yu Y, Shi S, et al.. Multi-agent deep reinforcement learning for multiple anesthetics collaborative control (PREPRINT). arXiv. 2025, 6:23-5.","journal-title":"arXiv"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2022.e12481","article-title":"Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network","volume":"9","author":"Ren W","year":"2023","unstructured":"Ren W, Chen J, Liu J, Fu Z, Yao Y, Chen X, Teng L. Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network. Heliyon. 2023, 9:e12481. 10.1016\/j.heliyon.2022.e12481","journal-title":"Heliyon"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.46632\/jeae\/2\/1\/18","article-title":"Random Forest in Closed Loop Control of Anesthesia. Journal of Emerging Technologies and Advanced Engineering","author":"Saju S","unstructured":"Saju S, Nathan V, Kavitha G, et al.. Random Forest in Closed Loop Control of Anesthesia. Journal of Emerging Technologies and Advanced Engineering. 2023. 10.46632\/jeae\/2\/1\/18"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.20944\/preprints202502.0065.v1","article-title":"Development of AI model for precision anaesthesia dosage optimization","volume":"4","author":"Aguda OE","year":"2025","unstructured":"Aguda OE, Okodeh SO, Muslehat AA, Nwoye EO. Development of AI model for precision anaesthesia dosage optimization. Preprints. 2025, 4:32-4. 10.20944\/preprints202502.0065.v1","journal-title":"Preprints"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.31185\/wjps.160","article-title":"Comprehensive review on automated control of anesthesia: recent methods, challenges and future trends","volume":"2","author":"Toma A","year":"2023","unstructured":"Toma A, Sahib M. Comprehensive review on automated control of anesthesia: recent methods, challenges and future trends. World J Pharm Sci. 2023, 2:291-315. 10.31185\/wjps.160","journal-title":"World J Pharm Sci"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s44254-023-00047-x","article-title":"Anesthetic drug discovery with computer-aided drug design and machine learning","volume":"2","author":"Liu X","year":"2024","unstructured":"Liu X, Xue Z, Luo M, Ke B, Lv J. Anesthetic drug discovery with computer-aided drug design and machine learning. Anesthesiol Perioper Sci. 2024, 2:16-8. 10.1007\/s44254-023-00047-x","journal-title":"Anesthesiol Perioper Sci"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1097\/ACO.0000000000001529","article-title":"The future of target-controlled infusion and new pharmacokinetic models","volume":"38","author":"Absalom AR","year":"2025","unstructured":"Absalom AR, Schnider TW. The future of target-controlled infusion and new pharmacokinetic models. Curr Opin Anaesthesiol. 2025, 38:375-81. 10.1097\/ACO.0000000000001529","journal-title":"Curr Opin Anaesthesiol"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.33137\/utmj.v102i2.45043","article-title":"When algorithms meet anesthesia: a new era of patient safety","volume":"102","author":"Saran E","year":"2025","unstructured":"Saran E. When algorithms meet anesthesia: a new era of patient safety. Univ Tor Med J. 2025, 102:32-4. 10.33137\/utmj.v102i2.45043","journal-title":"Univ Tor Med J"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0000000000006672","article-title":"Perioperative fluid and vasopressor therapy in 2050: from experimental medicine to personalization through automation","volume":"138","author":"Coeckelenbergh S","year":"2024","unstructured":"Coeckelenbergh S, Vincent JL, Duranteau J, Joosten A, Rinehart J. Perioperative fluid and vasopressor therapy in 2050: from experimental medicine to personalization through automation. Anesth Analg. 2024, 138:284-94. 10.1213\/ANE.0000000000006672","journal-title":"Anesth Analg"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.anclin.2025.05.006","article-title":"Advancements in neuroanesthesia through artificial intelligence","volume":"43","author":"Nozari A","year":"2025","unstructured":"Nozari A, Binda DD, Baker MB. Advancements in neuroanesthesia through artificial intelligence. Anesthesiol Clin. 2025, 43:527-47. 10.1016\/j.anclin.2025.05.006","journal-title":"Anesthesiol Clin"},{"key":"ref19","article-title":"Automatic ultrasound image segmentation of supraclavicular nerve using dilated U-Net deep learning architecture","volume":"4","author":"Miyatake M","year":"2022","unstructured":"Miyatake M, Nerella S, Simpson D, Pawlowicz N, Stern S, Tighe PJ, Rashidi P. Automatic ultrasound image segmentation of supraclavicular nerve using dilated U-Net deep learning architecture. arXiv. 2022, 4:23-5.","journal-title":"arXiv"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.anclin.2025.05.002","article-title":"Artificial intelligence in anesthesiology drug administration","volume":"43","author":"Ye J","year":"2025","unstructured":"Ye J, Bronstein S. Artificial intelligence in anesthesiology drug administration. Anesthesiol Clin. 2025, 43:431-51. 10.1016\/j.anclin.2025.05.002","journal-title":"Anesthesiol Clin"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1097\/EJA.0000000000002234","article-title":"Anaesthesia in 2050: how emerging technologies will transform our practice: The Sir Robert Macintosh Lecture 2025","volume":"42","author":"Leslie K","year":"2025","unstructured":"Leslie K. Anaesthesia in 2050: how emerging technologies will transform our practice: The Sir Robert Macintosh Lecture 2025. Eur J Anaesthesiol. 2025, 42:951-7. 10.1097\/EJA.0000000000002234","journal-title":"Eur J Anaesthesiol"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3390\/jcm14196900","article-title":"Artificial intelligence in anesthesia: enhancing precision, safety, and global access through data-driven systems","volume":"14","author":"Giri R","year":"2025","unstructured":"Giri R, Firdhos SH, Vida TA. Artificial intelligence in anesthesia: enhancing precision, safety, and global access through data-driven systems. J Clin Med. 2025, 14:26-8. 10.3390\/jcm14196900","journal-title":"J Clin Med"},{"key":"ref23","article-title":"A comprehensive survey on recent deep learning-based methods applied to surgical data (PREPRINT)","volume":"4","author":"Ali M","year":"2022","unstructured":"Ali M, Pena R, Ruiz G, Ali S. A comprehensive survey on recent deep learning-based methods applied to surgical data (PREPRINT). arXiv. 2022, 4:14-6.","journal-title":"arXiv"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1089\/cbr.2025.0063","article-title":"Radiopharmaceutical advancements in anesthesia and orthopedic oncology: a paradigm shift in cancer biotherapy and targeted therapy","volume":"40","author":"Su S","year":"2025","unstructured":"Su S, Zheng W, Huang M. Radiopharmaceutical advancements in anesthesia and orthopedic oncology: a paradigm shift in cancer biotherapy and targeted therapy. Cancer Biother Radiopharm. 2025, 40:458-69. 10.1089\/cbr.2025.0063","journal-title":"Cancer Biother Radiopharm"},{"key":"ref25","article-title":"Learning to perform low-contact autonomous nasotracheal intubation by recurrent action-confidence chunking with transformer (PREPRINT)","volume":"5","author":"Tian Y","year":"2025","unstructured":"Tian Y, Hao R, Huang Y, Xie D, Ling Chan CP, Kuen Chan JY, Ren H. Learning to perform low-contact autonomous nasotracheal intubation by recurrent action-confidence chunking with transformer (PREPRINT). arXiv. 2025, 5:41-3.","journal-title":"arXiv"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2025.17235","article-title":"Individualized perioperative blood pressure management in patients undergoing major abdominal surgery: the IMPROVE-multi Randomized Clinical Trial","volume":"9","author":"Saugel B","year":"2025","unstructured":"Saugel B, Meidert AS, Brunkhorst FM, et al.. Individualized perioperative blood pressure management in patients undergoing major abdominal surgery: the IMPROVE-multi Randomized Clinical Trial. JAMA. 2025, 9:52-4. 10.1001\/jama.2025.17235","journal-title":"JAMA"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.7326\/M18-1376","article-title":"PROBAST: a tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff RF","year":"2019","unstructured":"Wolff RF, Moons KG, Riley RD, et al.. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019, 170:51-8. 10.7326\/M18-1376","journal-title":"Ann Intern Med"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.46658\/JBIMES-20-08","article-title":"Systematic reviews of etiology and risk","author":"Moola S","year":"2020","unstructured":"Moola S, Munn Z, Tufanaru C, et al.. Systematic reviews of etiology and risk. JBI Manual for Evidence Synthesis. Joanna Briggs Institute, Adelaide, Australia; 2020. 4:61-4. 10.46658\/JBIMES-20-08"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/431313-a-systematic-literature-review-of-precision-anesthesia-through-machine-learning-automated-drug-titration-and-real-time-physiologic-optimization","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T14:23:27Z","timestamp":1764080607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/431313-a-systematic-literature-review-of-precision-anesthesia-through-machine-learning-automated-drug-titration-and-real-time-physiologic-optimization"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,25]]},"references-count":28,"URL":"https:\/\/doi.org\/10.7759\/cureus.97747","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11,25]]}}}