Artificial Intelligence supported ventilator management: Current applications, clinical evidence, and future directions

Shun Nakahara, Shunsuke Kondo, Abulhassan Ali, Collin Clarke, Scott Nishioka, Ehab G Daoud

Abstract

Background

Mechanical ventilation remains a complex intervention requiring continuous adjustment to balance oxygenation, ventilation, lung protection, patient–ventilator synchrony, and readiness for liberation. Artificial intelligence (AI) may support more individualized ventilator management by integrating multidimensional intensive care unit data.

Methods

We conducted a targeted narrative review of PubMed and Embase for studies evaluating AI, machine learning, deep learning, reinforcement learning, and closed-loop systems in ventilator management.

Findings

AI applications in ventilator management include outcome prediction, patient–ventilator asynchrony detection, ventilator setting optimization, and closed-loop ventilation. Current studies show promising performance in predicting extubation and weaning outcomes, detecting asynchrony from ventilator waveforms, forecasting high risk ventilation patterns, and supporting automated ventilator adjustments. However, most evidence remains retrospective, singlecenter, simulation based, or focused on model performance rather than patient centered outcomes.

Conclusions

AI-supported ventilator management is a rapidly evolving field with potential to enhance individualized respiratory care. Future studies should prioritize external validation, prospective trials, workflow integration, explainability, and safety frameworks to determine whether AI can improve clinically meaningful outcomes in mechanically ventilated patients.

Keywords: artificial intelligence, mechanical ventilation, ventilator management, patient–ventilator asynchrony, closed-loop ventilation

References

1. Rackley CR. Monitoring during mechanical ventilation. Respir Care 2020; 65(6):832-846. https://doi.org/10.4187/respcare.07812 PMid:32457174

2. Park KJ. Lung-protective ventilation strategy in acute respiratory distress syndrome: a critical reappraisal of current practice. Crit Care 2025 ;29(1):444. https://doi.org/10.1186/s13054-025-05675-2 PMid:41121191

3. Major VJ, Chiew YS, Shaw GM, et al. Biomedical engineer’s guide to the clinical aspects of intensive care mechanical ventilation. Biomed Eng Online 2018; 17(1):169. https://doi.org/10.1186/s12938-018-0599-9 PMid:30419903 PMCid:PMC6233601

4. Qadir N, Bartz RR, Cooter ML, et al. Variation in early management practices in moderate-to-severe ARDS in the United States: The severe ARDS: Generating evidence study. Chest 2021; 160(4):1304-1315. https://doi.org/10.1016/j.chest.2021.05.047 PMid:34089739 PMCid:PMC8176896

5. Goligher EC, Ferguson ND, Brochard LJ. Clinical challenges in mechanical ventilation. Lancet 2016; 387(10030):1856-1866. https://doi.org/10.1016/S0140-6736(16)30176-3 PMid:27203509

6. Peine A, Hallawa A, Bickenbach J, et al. Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care. NPJ Digit Med. 2021;4(1):32. https://doi.org/10.1038/s41746-021-00388-6 PMid:33608661 PMCid:PMC7895944

7. Viderman D, Ayazbay A, Kalzhan B, et al. Artificial intelligence in the management of patients with respiratory failure requiring mechanical ventilation: A scoping review. J Clin Med 2024; 13(24):7535. https://doi.org/10.3390/jcm13247535 PMid:39768462 PMCid:PMC11728182

8. Rubulotta F, Blanch Torra L, Naidoo KD, et al. Mechanical ventilation, past, present, and future. Anesth Analg 2024; 138(2):308-325. https://doi.org/10.1213/ANE.0000000000006701 PMid:38215710

9. Merola R, Battaglini D, Schultz MJ, et al. Physiology-guided personalized mechanical ventilation to prevent ventilator-induced lung injury. Front Med (Lausanne) 2026; 13(1764151):1764151. https://doi.org/10.3389/fmed.2026.1764151 PMid:41767526 PMCid:PMC12937159

10. Moralez GM, Amado F, Liu VX, et al. Data-driven quality of care in the ICU: A concise review. Crit Care Med 2025; 53(12):e2720-e2728. https://doi.org/10.1097/CCM.0000000000006862 PMid:40970767

11. Berkhout WEM, van Wijngaarden JJ, Workum JD, et al. Operationalization of artificial intelligence applications in the Intensive Care unit: A systematic review: A systematic review. JAMA Netw Open 2025; 8(7):e2522866. https://doi.org/10.1001/jamanetworkopen.2025.22866 PMid:40699572 PMCid:PMC12287835

12. Greco M, Caruso PF, Cecconi M. Artificial intelligence in the intensive care unit. Semin Respir Crit Care Med 2021; 42(1):2-9. https://doi.org/10.1055/s-0040-1719037 PMid:33152770

13. Sun K, Roy A, Tobin JM. Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research. J Crit Care 2024; 82(154792):154792. https://doi.org/10.1016/j.jcrc.2024.154792 PMid:38554543

14. Tungushpayev M, Suleimenova D, Sarria-Santamerra A, et al. The value of machine and deep learning in management of critically ill patients: An umbrella review. Int J Med Inform 2025; 204(106081):106081. https://doi.org/10.1016/j.ijmedinf.2025.106081 PMid:40795609

15. Otten M, Jagesar AR, Dam TA, et al. Does reinforcement learning improve outcomes for critically ill patients? A systematic review and level-of-readiness assessment. Crit Care Med 2024; 52(2):e79-e88. https://doi.org/10.1097/CCM.0000000000006100 PMid:37938042

16. Pai KC, Su SA, Chan MC, et al. Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan. BMC Anesthesiol 2022; 22(1):351. https://doi.org/10.1186/s12871-022-01888-y PMid:36376785 PMCid:PMC9664699

17. Zhu Y, Zhang J, Wang G, et al. Machine learning prediction models for mechanically ventilated patients: Analyses of the MIMIC-III database. Front Med (Lausanne) 2021; 8:662340. https://doi.org/10.3389/fmed.2021.662340 PMid:34277655 PMCid:PMC8280779

18. Igarashi Y, Ogawa K, Nishimura K, et al. Machine learning for predicting successful extubation in patients receiving mechanical ventilation. Front Med (Lausanne). 2022; 9:961252. https://doi.org/10.3389/fmed.2022.961252 PMid:36035403 PMCid:PMC9403066

19. Jiang G, Ma J, Xu H, et al. Application progress of machine learning in patient-ventilator asynchrony during mechanical ventilation: a systematic review. Crit Care 2025; 29(1):295. https://doi.org/10.1186/s13054-025-05523-3 PMid:40640842 PMCid:PMC12243175

20. Jia Y, Kaul C, Lawton T, et al. Prediction of weaning from mechanical ventilation using Convolutional Neural Networks. Artif Intell Med 2021; 117(102087):102087. https://doi.org/10.1016/j.artmed.2021.102087 PMid:34127233

21. Mamandipoor B, Frutos-Vivar F, Peñuelas O, et al. Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation. BMC Med Inform Decis Mak 2021; 21(1):152. https://doi.org/10.1186/s12911-021-01506-w PMid:33962603 PMCid:PMC8102841

22. Wang Y, Bai Y, Jin G. Explainable deep-learning models to predict diaphragmatic dysfunction and cognitive stress in ICU patients under mechanical ventilation. Front Physiol 2026; 17(1765898):1765898. https://doi.org/10.3389/fphys.2026.1765898 PMid:42005315 PMCid:PMC13082985

23. Liu S, See KC, Ngiam KY, et al. Reinforcement learning for clinical decision support in critical care: Comprehensive review. J Med Internet Res 2020; 22(7):e18477. https://doi.org/10.2196/18477 PMid:32706670 PMCid:PMC7400046

24. Liu S, Xu Q, Xu Z, et al. Reinforcement learning to optimize ventilator settings for patients on invasive mechanical ventilation: Retrospective study. J Med Internet Res 2024; 26:e44494. https://doi.org/10.2196/44494 PMid:39219230 PMCid:PMC11525081

25. Fenske SW, Peltekian A, Kang M, et al. Developing and validating machine learning models to predict next-day extubation. Sci Rep 2025; 15(1):27552. https://doi.org/10.1038/s41598-025-12264-4 PMid:40731125 PMCid:PMC12307926

26. Otaguro T, Tanaka H, Igarashi Y, et al. Machine learning for prediction of successful extubation of mechanical ventilated patients in an intensive care unit: A retrospective observational study. J Nippon Med Sch 2021; 88(5):408-417. https://doi.org/10.1272/jnms.JNMS.2021_88-508 PMid:33692291

27. Fabregat A, Magret M, Ferré JA, et al. A machine learning decision-making tool for extubation in Intensive Care Unit patients. Comput Methods Programs Biomed 2021; 200(105869):105869. https://doi.org/10.1016/j.cmpb.2020.105869 PMid:33250280

28.Zhao QY, Wang H, Luo JC, et al. Development and validation of a machine-learning model for prediction of extubation failure in Intensive Care units. Front Med (Lausanne) 2021; 8:676343. https://doi.org/10.3389/fmed.2021.676343 PMid:34079812 PMCid:PMC8165178

29. Park JE, Kim DY, Park JW, et al. Development of a machine learning model for predicting weaning outcomes based solely on continuous ventilator parameters during spontaneous breathing trials. Bioengineering (Basel) 2023; 10(10):1163. https://doi.org/10.3390/bioengineering10101163 PMid:37892893 PMCid:PMC10604888

30. Liu CF, Hung CM, Ko SC, et al. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Front Med (Lausanne) 2022; 9:935366. https://doi.org/10.3389/fmed.2022.935366 PMid:36465940 PMCid:PMC9715756

31. Jiang Z, Liu L, Du L, et al. Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data. Heliyon 2024; 10(6):e28143. https://doi.org/10.1016/j.heliyon.2024.e28143 PMid:38533071 PMCid:PMC10963609

32. Zhang Z, Liu J, Xi J, et al. Derivation and validation of an ensemble model for the prediction of agitation in mechanically ventilated patients maintained under light sedation. Crit Care Med. 2021; 49(3):e279-e290. https://doi.org/10.1097/CCM.0000000000004821 PMid:33470778

33. Epstein SK. How often does patient-ventilator asynchrony occur and what are the consequences? Respir Care 2011; 56(1):25-38. https://doi.org/10.4187/respcare.01009 PMid:21235836

34. Thille AW, Rodriguez P, Cabello B, et al. Patient-ventilator asynchrony during assisted mechanical ventilation. Intensive Care Med 2006; 32(10):1515-1522. https://doi.org/10.1007/s00134-006-0301-8 PMid:16896854

35. Kyo M, Shimatani T, Hosokawa K, et al. Patient-ventilator asynchrony, impact on clinical outcomes and effectiveness of interventions: a systematic review and meta-analysis. J Intensive Care 2021; 9(1):50. https://doi.org/10.1186/s40560-021-00565-5 PMid:34399855 PMCid:PMC8365272

36. de Bie MJ, Rietveld PJ, van der Velde-Quist F, et al. The association between patient-ventilator asynchrony and clinical outcomes in mechanically ventilated patients: A systematic review. Crit Care Med 2025; 53(11):e2261-e2270. https://doi.org/10.1097/CCM.0000000000006816 PMid:40793855 PMCid:PMC12577656

37. Ramírez II, Gutiérrez-Arias R, Damiani LF, et al. Specific training improves the detection and management of patient-ventilator asynchrony. Respir Care 2024; 69(2):166-175. https://doi.org/10.4187/respcare.11329 PMid:38267230 PMCid:PMC10898470

38. Sottile PD, Albers D, Higgins C, et al. The association between ventilator dyssynchrony, delivered tidal volume, and sedation using a novel automated ventilator dyssynchrony detection algorithm. Crit Care Med 2018; 46(2):e151-e157. https://doi.org/10.1097/CCM.0000000000002849 PMid:29337804 PMCid:PMC5772880

39. Blanch L, Villagra A, Sales B, et al. Asynchronies during mechanical ventilation are associated with mortality. Intensive Care Med 2015; 41(4):633-641. https://doi.org/10.1007/s00134-015-3692-6 PMid:25693449

40. Rietveld TP, van der Ster BJP, Schoe A, et al. Let’s get in sync: current standing and future of AI-based detection of patient-ventilator asynchrony. Intensive Care Med Exp 2025; 13(1):39. https://doi.org/10.1186/s40635-025-00746-8 PMid:40119215 PMCid:PMC11928342

41. de Haro C, Santos-Pulpón V, Telías I, et al. Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques. Crit Care 2024; 28(1):75. https://doi.org/10.1186/s13054-024-04845-y PMid:38486268 PMCid:PMC10938655

42. Baedorf-Kassis EN, Glowala J, Póka KB, et al. Reverse triggering neural network and rules-based automated detection in acute respiratory distress syndrome. J Crit Care 2023; 75(154256):154256. https://doi.org/10.1016/j.jcrc.2023.154256 PMid:36701820 PMCid:PMC10173144

43. Muñoz J, Ruíz-Cacho R, Fernández-Araujo NJet al. Artificial intelligence in the management of patient-ventilator asynchronies: A scoping review. Heart Lung 2025; 73:139-152. https://doi.org/10.1016/j.hrtlng.2025.05.003 PMid:40412305

44. Sousa MLEA, Magrans R, Hayashi FK, et al. Clusters of double triggering impact clinical outcomes: Insights from the EPIdemiology of patient-ventilator aSYNChrony (EPISYNC) cohort study: Insights from the EPIdemiology of patient-ventilator aSYNChrony (EPISYNC) cohort study. Crit Care Med 2021; 49(9):1460-1469. https://doi.org/10.1097/CCM.0000000000005029 PMid:33883458

45. Slutsky AS, Ranieri VM. Ventilator-induced lung injury. N Engl J Med 2013; 369(22):2126-2136. https://doi.org/10.1056/NEJMra1208707 PMid:24283226 PMCid:PMC10264327

46. van den Berg MJW, Heunks L, Doorduin J. Advances in achieving lung and diaphragm-protective ventilation. Curr Opin Crit Care 2025; 31(1):38-46. https://doi.org/10.1097/MCC.0000000000001228 PMid:39560149 PMCid:PMC11676617

47. Serpa Neto A, Cardoso SO, Manetta JA, et al. Association between use of lung-protective ventilation with lower tidal volumes and clinical outcomes among patients without acute respiratory distress syndrome: a meta-analysis: A meta-analysis. JAMA 2012; 308(16):1651-1659. https://doi.org/10.1001/jama.2012.13730 PMid:23093163

48. Kondo S, Corpuz A, Clarke C, et al. Mechanical power in mechanical ventilation: Physiologic basis, evidence, and clinical implications. J Mech Vent 2026; 7(1):1-15. https://doi.org/10.53097/JMV.10144

49. Gattinoni L, Tonetti T, Cressoni M, et al. Ventilator-related causes of lung injury: the mechanical power. Intensive Care Med 2016; 42(10):1567-1575. https://doi.org/10.1007/s00134-016-4505-2 PMid:27620287

50. Sato R, Kondo S, Ali A, et al. The association between mechanical power and mortality in critically ill patients receiving invasive mechanical ventilation: A systematic review and meta-analysis. Crit Care Med 2026; 54(7):1742-1754. https://doi.org/10.1097/CCM.0000000000007146 PMid:42153811

51. Misseri G, Piattoli M, Cuttone G, et al. Artificial intelligence for mechanical ventilation: A transformative shift in critical care. Ther Adv Pulm Crit Care Med 2024; 19:29768675241298918. https://doi.org/10.1177/29768675241298918 PMid:39534716 PMCid:PMC11555733

52. Ossai CI, Wickramasinghe N. Intelligent decision support with machine learning for efficient management of mechanical ventilation in the intensive care unit – A critical overview. Int J Med Inform 2021; 150(104469):104469. https://doi.org/10.1016/j.ijmedinf.2021.104469 PMid:33906020

53. Hagan R, Gillan CJ, Spence I, et al. Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in Intensive Care Units. Comput Biol Med 2020; 126(104030):104030. https://doi.org/10.1016/j.compbiomed.2020.104030 PMid:33068808 PMCid:PMC7543875

54. Ruiz-Botella M, Manrique S, Gomez J, et al. Advancing ICU patient care with a Real-Time predictive model for mechanical Power to mitigate VILI. Int J Med Inform 2024; 189(105511):105511. https://doi.org/10.1016/j.ijmedinf.2024.105511 PMid:38851133

55. Rose L, Schultz MJ, Cardwell CR, et al. Automated versus non-automated weaning for reducing the duration of mechanical ventilation for critically ill adults and children. Cochrane Database Syst Rev 2025; 7(7):CD009235. https://doi.org/10.1002/14651858.CD009235.pub4 PMid:40678933 PMCid:PMC12272810

56. Sinnige JS, Buiteman-Kruizinga LA, Horn J, et al. Effect of automated closed-loop ventilation vs protocolized conventional ventilation on ventilator-free days in critically ill adults: A randomized clinical trial: A randomized clinical trial. JAMA 2026;335(10):874-884. https://doi.org/10.1001/jama.2025.24384 PMid:41361939 PMCid:PMC12687210

57. Burns KEA, Lellouche F, Nisenbaum R, et al. Automated weaning and SBT systems versus non-automated weaning strategies for weaning time in invasively ventilated critically ill adults. Cochrane Database Syst Rev 2014; 2018(9):CD008638. https://doi.org/10.1002/14651858.CD008638.pub2 PMid:25203308 PMCid:PMC6516852

58. Platen P von, Pomprapa A, Lachmann B, et al. The dawn of physiological closed-loop ventilation-a review. Crit Care 2020; 24(1):121. https://doi.org/10.1186/s13054-020-2810-1 PMid:32223754 PMCid:PMC7104522

59. Arnal JM, Katayama S, Howard C. Closed-loop ventilation. Curr Opin Crit Care 2023; 29(1):19-25. https://doi.org/10.1097/MCC.0000000000001012 PMid:36484170

60. Botta M, Wenstedt EFE, Tsonas AM, et al. Effectiveness, safety and efficacy of INTELLiVENT adaptive support ventilation, a closed-loop ventilation mode for use in ICU patients – a systematic review. Expert Rev Respir Med 2021; 15(11):1403-1413. https://doi.org/10.1080/17476348.2021.1933450 PMid:34047244

61. Arnal JM, Daoud EG. Guidelines on setting the target minute ventilation in Adaptive Support Ventilation. J Mech Vent 2021; 2(3):80-85. https://doi.org/10.53097/JMV.10029

62. Bialais E, Wittebole X, Vignaux L, et al. Closed-loop ventilation mode (IntelliVent®-ASV) in intensive care unit: a randomized trial. Minerva Anestesiol 2016; 82(6):657-668. PMID: 26957117.

63. Goossen RL, Schultz MJ, Tschernko E, et al. Effects of closed loop ventilation on ventilator settings, patient outcomes and ICU staff workloads – A systematic review. Eur J Anaesthesiol 2024; 41(6):438-446. https://doi.org/10.1097/EJA.0000000000001972 PMid:38385449 PMCid:PMC11064903

64. Gallifant J, Zhang J, Del Pilar Arias Lopez M, et al. Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias. Br J Anaesth 2022; 128(2):343-351. https://doi.org/10.1016/j.bja.2021.09.025 PMid:34772497 PMCid:PMC8792831

65. Muñoz J, Fernández-Araujo NJ, Ruíz-Cacho R, et al. Artificial intelligence and computerized decision support in adult intensive care: A systematic review of randomized controlled trials. J Crit Care 2026; 94(155600):155600. https://doi.org/10.1016/j.jcrc.2026.155600 PMid:42048766

66. Pinsky MR, Bedoya A, Bihorac A, et al. Use of artificial intelligence in critical care: opportunities and obstacles. Crit Care 2024; 28(1):113. https://doi.org/10.1186/s13054-024-04860-z PMid:38589940 PMCid:PMC11000355

67. Murali M, Ni M, Karbing DS, et al. Clinical practice, decision-making, and use of clinical decision support systems in invasive mechanical ventilation: a narrative review. Br J Anaesth 2024; 133(1):164-177. https://doi.org/10.1016/j.bja.2024.03.011 PMid:38637268 PMCid:PMC11213991

68. Böhm-Hustede AK, Lubasch JS, Hoogestraat AT, et al. Barriers and facilitators to the implementation and adoption of computerised clinical decision support systems: an overview of reviews. Syst Rev. 2025;14(1):2. https://doi.org/10.1186/s13643-024-02745-4 PMid:39748437 PMCid:PMC11697958

69. Agard G, Hraiech S, Gauss T. From promise to practice: A roadmap for artificial intelligence in critical care. J Crit Care 2026; 91(155263):155263. https://doi.org/10.1016/j.jcrc.2025.155263 PMid:40966881

70. Idan D, Einav S. Primer on large language models: an educational overview for intensivists. Crit Care 2025; 29(1):238. https://doi.org/10.1186/s13054-025-05479-4 PMid:40506762 PMCid:PMC12164094

71. Huo B, Boyle A, Marfo N, et al. Large language models for chatbot health advice studies: A systematic review. JAMA Netw Open 2025; 8(2):e2457879. https://doi.org/10.1001/jamanetworkopen.2024.57879 PMid:39903463 PMCid:PMC11795331

72. Chow M, Li AY, Quek DYJ, et al. The cognitive ecology of medicine: generative AI, clinical reasoning, and the preservation of adaptive expertise. Postgrad Med J 2026;qgag068. https://doi.org/10.1093/postmj/qgag068 PMid:42213533

73. Armitage RC. Implications of large language models for clinical practice: Ethical analysis through the principlism framework. J Eval Clin Pract 2025; 31(1):e14250. https://doi.org/10.1111/jep.14250 PMid:39618089 PMCid:PMC11609490

74. Farquhar S, Kossen J, Kuhn L, et al. Detecting hallucinations in large language models using semantic entropy. Nature 2024; 630(8017):625-630. https://doi.org/10.1038/s41586-024-07421-0 PMid:38898292 PMCid:PMC11186750

75. Ferryman K, Mackintosh M, Ghassemi M. Considering biased data as informative artifacts in AI-assisted health care. N Engl J Med 2023; 389(9):833-838. https://doi.org/10.1056/NEJMra2214964 PMid:37646680

76. Workum JD, Meyfroidt G, Bakker J, et al. AI in critical care: A roadmap to the future. J Crit Care 2026; 91(155262):155262. https://doi.org/10.1016/j.jcrc.2025.155262 PMid:40992126

77. Abgrall G, Holder AL, Chelly Dagdia Z, et al. Should AI models be explainable to clinicians? Crit Care 2024; 28(1):301. https://doi.org/10.1186/s13054-024-05005-y PMid:39267172 PMCid:PMC11391805

78. Palmieri S, Robertson CT, Cohen IG. New guidance on responsible use of AI. JAMA 2026; 335(3):207-208. https://doi.org/10.1001/jama.2025.23059 PMid:41335417