Yoğun Bakım ve Resüsitasyon Yönetiminde Yapay Zekâ
Özet
Yapay zekâ (YZ), yoğun bakım ve resüsitasyon yönetiminde hızla dönüştürücü bir rol üstlenmektedir. Bu alanlarda saniyeler kritik öneme sahiptir ve klinik karar verme süreci son derece karmaşıktır. Yoğun bakım üniteleri, yatak başı monitörlerden, laboratuvar sistemlerinden, görüntülemelerden ve elektronik sağlık kayıtlarından sürekli veri üretmektedir. Makine öğrenimi, derin öğrenme, doğal dil işleme ve bilgisayarla görme gibi YZ teknikleri, bu büyük ve heterojen veri kümelerini analiz ederek klinik kötüleşmenin erken tanınmasını, hasta sınıflandırmasını ve kişiselleştirilmiş tedavi stratejilerini desteklemektedir. Geleneksel statik skorlama sistemlerinden farklı olarak, YZ modelleri sürekli öğrenerek gerçek zamanlı ve dinamik karar desteği sağlamaktadır. Resüsitasyon bağlamında YZ; kardiyak arrestin öngörülmesi, yüksek kaliteli kardiyopulmoner resüsitasyonun (CPR) yönlendirilmesi ve prognoz tahmininde kullanılmaktadır. Gelişmiş algoritmalar ritim yorumlama doğruluğunu artırmakta, gereksiz şokları azaltmakta ve robotik kompresyon sistemleri gibi yeni çözümleri araştırmaktadır. Resüsitasyon sonrası dönemde ise YZ; nörolojik prognozun öngörülmesi, hedeflenmiş ısı yönetiminin optimize edilmesi ve uzun vadeli rehabilitasyonun desteklenmesiyle hasta sağkalımını ve yaşam kalitesini iyileştirmektedir. Bölüm ayrıca veri yanlılığı, algoritmik şeffaflık, etik kaygılar ve düzenleyici zorluklara dikkat çekmektedir. Sonuç olarak, YZ klinik yargının yerini almak yerine onu güçlendiren, hassas, verimli ve hasta odaklı bir bakım modeli sunmaktadır.
Referanslar
Hirani R, Noruzi K, Khuram H, et al. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel). 2024;14(5):557. Published 2024 Apr 26. doi:10.3390/life14050557
Prabhudutta R , Sachin S , Raj R Application of Artificial Intelligence Techniques in the Intensive Care Unit. IJRES, 2023;12(1), 42–50. DOI: 10.11591/ijres.v12.i1.pp42-50
O'Reilly D, McGrath J, Martin-Loeches I. Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future. J Intensive Med. 2023;4(1):34-45. Published 2023 Nov 29. doi:10.1016/j.jointm.2023.10.001
Li F, Wang S, Gao Z, et al. Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring. Front Med (Lausanne). 2025;11:1510792. Published 2025 Jan 6. doi:10.3389/fmed.2024.1510792
Gutierrez G. Artificial Intelligence in the Intensive Care Unit [published correction appears in Crit Care. 2024 Mar 21;28(1):94. doi: 10.1186/s13054-024-04856-9.]. Crit Care. 2020;24(1):101. Published 2020 Mar 24. doi:10.1186/s13054-020-2785-y
Bertoni M, Spadaro S, Goligher EC. Monitoring Patient Respiratory Effort During Mechanical Ventilation: Lung and Diaphragm-Protective Ventilation [published correction appears in Crit Care. 2024 Mar 21;28(1):94. doi: 10.1186/s13054-024-04856-9.]. Crit Care. 2020;24(1):106. Published 2020 Mar 24. doi:10.1186/s13054-020-2777-y
Yang S, Galvagno S, Badjatia N, et al. A Novel Continuous Real-Time Vital Signs Viewer for Intensive Care Units: Design and Evaluation Study. JMIR Hum Factors. 2024;11:e46030. Published 2024 Jan 5. doi:10.2196/46030
Keim-Malpass J, Clark MT, Lake DE, Moorman JR. Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring. J Clin Monit Comput. 2020;34(4):797-804. doi:10.1007/s10877-019-00361-5
Biesheuvel LA, Dongelmans DA, Elbers PWG. Artificial intelligence to advance acute and intensive care medicine. Curr Opin Crit Care. 2024;30(3):246-250. doi:10.1097/MCC.0000000000001150
Banerji CRS, Bhardwaj Shah A, Dabson B, et al. Clinicians must participate in the development of multimodal AI. EClinicalMedicine. 2025;84:103252. Published 2025 May 23. doi:10.1016/j.eclinm.2025.103252
Bignami EG, Berdini M, Panizzi M, et al. Artificial Intelligence in Sepsis Management: An Overview for Clinicians. J Clin Med. 2025;14(1):286. Published 2025 Jan 6. doi:10.3390/jcm14010286
B H, D K M, T M R, et al. Advances in diagnosis and prognosis of bacteraemia, bloodstream infection, and sepsis using machine learning: A comprehensive living literature review. Artif Intell Med. 2025;160:103008. doi:10.1016/j.artmed.2024.103008
Suresh V, Singh KK, Vaish E, et al. Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool. Cureus. 2024;16(5):e59797. Published 2024 May 7. doi:10.7759/cureus.59797
Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021;139(1):4-15. doi:10.1093/bmb/ldab016
Hadweh P, Niset A, Salvagno M, Al Barajraji M, El Hadwe S, Taccone FS, Barrit S. Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations from Rule-Based Systems to Frontier Models. Journal of Clinical Medicine. 2025; 14(12):4026. https://doi.org/10.3390/jcm14124026
Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021;8(2):e188-e194. doi:10.7861/fhj.2021-0095
Mudgal SK, Agarwal R, Chaturvedi J, Gaur R, Ranjan N. Real-world application, challenges and implication of artificial intelligence in healthcare: an essay. Pan Afr Med J. 2022;43:3. Published 2022 Sep 2. doi:10.11604/pamj.2022.43.3.33384
Greco M, Angelotti G, Caruso PF, et al. Outcome prediction during an ICU surge using a purely data-driven approach: A supervised machine learning case-study in critically ill patients from COVID-19 Lombardy outbreak. Int J Med Inform. 2022;164:104807. doi:10.1016/j.ijmedinf.2022.104807
Hirani R, Noruzi K, Khuram H, et al. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel). 2024;14(5):557. Published 2024 Apr 26. doi:10.3390/life14050557
Ceylan B, Olmuşçelik O, Karaalioğlu B, et al. Predicting Severe Respiratory Failure in Patients with COVID-19: A Machine Learning Approach. J Clin Med. 2024;13(23):7386. Published 2024 Dec 4. doi:10.3390/jcm13237386
Sahm C, Kirschneck C, Proff P, Paddenberg-Schubert E. Predictors of changes in incisor inclination during orthodontic levelling and alignment with fixed appliances: a retrospective cross-sectional study. Head Face Med. 2025;21(1):41. Published 2025 May 26. doi:10.1186/s13005-025-00519-4
K B, Venkatesan L, Benjamin LS, K V, Satchi NS. Reinforcement Learning in Personalized Medicine: A Comprehensive Review of Treatment Optimization Strategies. Cureus. 2025;17(4):e82756. Published 2025 Apr 21. doi:10.7759/cureus.82756
Liu Y, Wang H, Zhou H, et al. A review of reinforcement learning for natural language processing and applications in healthcare. J Am Med Inform Assoc. 2024;31(10):2379-2393. doi:10.1093/jamia/ocae215
Hadweh P, Niset A, Salvagno M, et al. Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations from Rule-Based Systems to Frontier Models. J Clin Med. 2025;14(12):4026. Published 2025 Jun 6. doi:10.3390/jcm14124026
Okada Y, Mertens M, Liu N, Lam SSW, Ong MEH. AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges. Resusc Plus. 2023;15:100435. Published 2023 Jul 28. doi:10.1016/j.resplu.2023.100435
Miao J, Thongprayoon C, Cheungpasitporn W. Should Artificial Intelligence Be Used for Physician Documentation to Reduce Burnout?. Kidney360. 2024;5(5):765-767. doi:10.34067/KID.0000000000000430
Alamgir A, Mousa O, Shah Z. Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review. JMIR Med Inform. 2021;9(12):e30798. Published 2021 Dec 17. doi:10.2196/30798
Yuan S, Yang Z, Li J, Wu C, Liu S. AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis. BMC Med Inform Decis Mak. 2025;25(1):203. Published 2025 Jun 2. doi:10.1186/s12911-025-03048-x
Bora ES. Artificial Intelligence in Emergency Medicine. JEB Med Sci 2023;4(1):33-36. doi: 10.5606/jebms.2023.1043
Viderman D, Abdildin YG, Batkuldinova K, Badenes R, Bilotta F. Artificial Intelligence in Resuscitation: A Scoping Review. J Clin Med. 2023;12(6):2254. Published 2023 Mar 14. doi:10.3390/jcm12062254
Eshel R, Wacht O, Schwartz D. Real-Time Audiovisual Feedback Training Improves Cardiopulmonary Resuscitation Performance: A Controlled Study. Simul Healthc. 2019;14(6):359-365. doi:10.1097/SIH.0000000000000390
Gkintoni E, Antonopoulou H, Sortwell A, Halkiopoulos C. Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy. Brain Sci. 2025;15(2):203. Published 2025 Feb 15. doi:10.3390/brainsci15020203
Emami et al.: Emami, P. et al. (2024). The Future of CPR: Leveraging Artificial Intelligence. Journal of Tehran University Heart Center, 19(2), 77–78.
Asim et al.: Asim, R. et al. (2025). Multi-faceted Role of Artificial Intelligence in Cardiopulmonary Resuscitation. AI and Ethics, 5, 2015–2020.
Matteucci A, Pignalberi C, Di Fusco S, et al. Appropriate use of wearable defibrillators with multiparametric evaluation to avoid unnecessary defibrillator implantation. Open Heart. 2024;11(2):e002787. Published 2024 Sep 18. doi:10.1136/openhrt-2024-002787
Röger S, Rosenkaimer SL, Hohneck A, et al. Therapy optimization in patients with heart failure: the role of the wearable cardioverter-defibrillator in a real-world setting. BMC Cardiovasc Disord. 2018;18(1):52. Published 2018 Mar 15. doi:10.1186/s12872-018-0790-8
Okada Y, Mertens M, Liu N, Lam SSW, Ong MEH. AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges. Resusc Plus. 2023;15:100435. Published 2023 Jul 28. doi:10.1016/j.resplu.2023.100435
Emami P, Sistani M, Marzban A. The Future of CPR: Leveraging Artificial Intelligence for Enhanced Cardiopulmonary Resuscitation Outcomes. J Tehran Heart Cent. 2024;19(2):77-78. doi:10.18502/jthc.v19i2.16194
Kim T, Suh GJ, Kim KS, et al. Development of artificial intelligence-driven biosignal-sensitive cardiopulmonary resuscitation robot. Resuscitation. 2024;202:110354. doi:10.1016/j.resuscitation.2024.110354
Cha KC, Kim HI, Kim YW, et al. Comparison of hemodynamic effects and resuscitation outcomes between automatic simultaneous sterno-thoracic cardiopulmonary resuscitation device and LUCAS in a swine model of cardiac arrest. PLoS One. 2019;14(8):e0221965. Published 2019 Aug 30. doi:10.1371/journal.pone.0221965
Huang W, Shu N. AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders. Cell Rep Med. 2025;6(5):102132. doi:10.1016/j.xcrm.2025.102132
Aravazhi PS, Gunasekaran P, Benjamin NZY, et al. The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions. Dis Mon. 2025;71(6):101882. doi:10.1016/j.disamonth.2025.101882
Omairi AM, Pandey S. Targeted Temperature Management. [Updated 2023 Jun 25]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK556124/
Srivilaithon W, Muengtaweepongsa S. The Outcomes of Targeted Temperature Management After Cardiac Arrest at Emergency Department: A Real-World Experience in a Developing Country. Ther Hypothermia Temp Manag. 2017;7(1):24-29. doi:10.1089/ther.2016.0014
Schembari G, Santonocito C, Messina S, Caruso A, Cardia L, Rubulotta F, Noto A, Bignami EG, Sanfilippo F. Post-Intensive Care Syndrome as a Burden for Patients and Their Caregivers: A Narrative Review. Journal of Clinical Medicine. 2024; 13(19):5881. https://doi.org/10.3390/jcm13195881
Huang G, Chen X, Liao C. AI-Driven Wearable Bioelectronics in Digital Healthcare. Biosensors. 2025; 15(7):410. https://doi.org/10.3390/bios15070410
Dailah HG, Koriri M, Sabei A, Kriry T, Zakri M. Artificial Intelligence in Nursing: Technological Benefits to Nurse's Mental Health and Patient Care Quality. Healthcare (Basel). 2024;12(24):2555. Published 2024 Dec 18. doi:10.3390/healthcare12242555
Khawar MM, Abdus Saboor H, Eric R, et al. Role of artificial intelligence in predicting neurological outcomes in postcardiac resuscitation. Ann Med Surg (Lond). 2024;86(12):7202-7211. Published 2024 Oct 22. doi:10.1097/MS9.0000000000002673
Cruz-Gonzalez P, He AW, Lam EP, et al. Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications. Psychol Med. 2025;55:e18. Published 2025 Feb 6. doi:10.1017/S0033291724003295
Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel). 2024;11(4):337. Published 2024 Mar 29. doi:10.3390/bioengineering11040337
Hughes A, Shandhi MMH, Master H, Dunn J, Brittain E. Wearable Devices in Cardiovascular Medicine. Circ Res. 2023;132(5):652-670. doi:10.1161/CIRCRESAHA.122.322389
Khedraki R, Srivastava AV, Bhavnani SP. Framework for Digital Health Phenotypes in Heart Failure: From Wearable Devices to New Sensor Technologies. Heart Fail Clin. 2022;18(2):223-244. doi:10.1016/j.hfc.2021.12.003
Boltaboyeva A, Baigarayeva Z, Imanbek B, Ozhikenov K, Getahun AJ, Aidarova T, Karymsakova N. A Review of Innovative Medical Rehabilitation Systems with Scalable AI-Assisted Platforms for Sensor-Based Recovery Monitoring. Applied Sciences. 2025; 15(12):6840. https://doi.org/10.3390/app15126840
Rasa AR. Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent Advancements. Biomed Res Int. 2024;2024:9554590. Published 2024 Dec 17. doi:10.1155/bmri/9554590
Senadheera I, Hettiarachchi P, Haslam B, et al. AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI. Sensors (Basel). 2024;24(20):6585. Published 2024 Oct 12. doi:10.3390/s24206585
Pavon JM, Previll L, Woo M, et al. Machine learning functional impairment classification with electronic health record data. J Am Geriatr Soc. 2023;71(9):2822-2833. doi:10.1111/jgs.18383
Olawade DB, Clement David-Olawade A, Adereni T, Egbon E, Teke J, Boussios S. Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions. Diseases. 2025; 13(1):24. https://doi.org/10.3390/diseases13010024
