Acil Serviste Yapay Zekâ Destekli Karar Destek Sistemleri
Özet
Hasta yoğunluğunun fazla ve zamanın kısıtlı olduğu acil servislerde hızlı ve doğru karar verme çok önemlidir. Klinik Karar Destek Sistemleri, doktorların tanı, tedavi ve takip süreçlerine yardımcı olan bilgisayar programlarıdır. Karmaşık klinik durumlarda geleneksel kural tabanlı sistemler yeterli gelmemektedir. Bu sistemlere yapay zekânın eklenmesi, sistemleri daha esnek, uyarlanabilir hale getirmiştir. Yapay zekâ destekli klinik karar destek sistemleri, büyük ve çeşitli veri kümelerini analiz etmek, tanıları daha doğru hale getirmek ve klinik sonuçları iyileştirmek için makine öğrenimi, derin öğrenme ve doğal dil işlemeyi kullanır. Acil servislerde, bu sistemler triyaj optimizasyonuna, komplikasyon riskini tahmin etmeye, tıbbi görüntüleri yorumlamaya ve kaynakları iyi yönetmeye yardımcı olur. Avantajları olmasına rağmen, veri güvenliği, algoritmik önyargı ve kullanıcı kabulü gibi sorunların göz ardı edilmemesi gerekmektedir. Yapay zekâ destekli klinik karar destek sistemleri yeni bir teknoloji olmasının yanı sıra, klinik karar vermeyi daha hızlı, güvenilir ve kanıtlara dayalı hale getirerek acil tıbbın çalışma şeklini de değiştirmektedir.
Referanslar
Michieletto S. Decision-making in emergency medicine. Emerg Med Australas. 2020; 32(6):1062-1063. doi:10.1111/1742-6723.13671
Patterson B, Pulia M, Ravi S, et al. Scope and influence of electronic health record-integrated clinical decision support in the emergency department: a systematic review. Ann Emerg Med. 2019; 74(2):285-296. doi:10.1016/j.annemergmed.2018.10.034.
Pendyala SK. Real-time analytics and clinical decision support systems: transforming emergency care. IJFMR. 2024; 6(6):1-12. doi:10.36948/ijfmr.2024.v06i06.31500.
Bennett P, Hardiker N. The use of computerized clinical decision support systems in emergency care: a substantive review of the literature. J Am Med Inform Assoc. 2017; 24(3):655–668. doi:10.1093/jamia/ocw151.
Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decision-support systems: a systemic review. Ann Intern Med. 2012; 157(1):29-43. doi:10.7326/0003-4819-157-1-201207030-00450.
Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020; 3:17. doi:10.1038/s41746-020-0221-y.
Bharmal AR. Transforming healthcare delivery: ai-powered clinical decision support systems. Int J Sci Res Comput Sci Eng Inf Technol. 2025; 11(1):339-347. doi:10.32628/cseit25111233.
Elhaddad M, Hamam S. AI-driven clinical decision support systems: an ongoing pursuit of potential. Cureus. 2024; 16(4): e57728. doi:10.7759/cureus.57728.
Tyler NS, Jacobs PG. Artificial intelligence in decision support systems for type 1 diabetes. Sensors (Basel). 2020; 20(11):3214. doi:10.3390/s20113214.
Kostopoulos G, Davrazos G, Kotsiantis S. Explainable artificial ıntelligence-based decision support systems: a recent review. Electronics. 2024; 13(14):2842. doi:10.3390/electronics13142842.
Vasey B, Nagendran M, Campbell B. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ. 2022; 377:e070904. doi:10.1136/bmj-2022-070904.
Fernandes M, Vieira SM, Leite F, Palos C, Finkelstein S, Sousa JMC. Clinical decision support systems for triage in the emergency department using intelligent systems: a review. Artif Intell Med. 2020; 102:101762. doi:10.1016/j.artmed.2019.101762.
Choi A, Lee K, Hyun H, et al. A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system. Sci Rep. 2024; 14(1):30116. doi:10.1038/s41598-024-80268-7.
Kuttan N, Pundkar A, Gadkari C, Patel A, Kumar A. Transforming emergency medicine with artificial intelligence: from triage to clinical decision support. Multidiscip Rev. 2025; 8: e2025285. doi:10.31893/multirev.2025285.
Boonstra A, Laven M. Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review. BMC Health Serv Res. 2022; 22(1):669. doi:10.1186/s12913-022-08070-7.
Yesankar P, Puri C, Gote PM. AI-powered clinical decision support systems (cdss): challenges, benefits, applications, and future directions. International Conference on Machine Learning and Autonomous Systems (ICMLAS). 2025; 1192-1197. doi:10.1109/ICMLAS64557.2025.10969014.
Oliveira IS, Vanin AA, Pena Costa L, et al. Profile of patients with acute low back pain who sought emergency departments: a cross-sectional study. Spine. 2020; 45(5):E296-303. doi:10.1097/BRS.0000000000003253.
Fleury MJ, Cao Z, Grenier G, Ferland F. Profiles of quality of life among patients using emergency departments for mental health reasons. Health Qual Life Outcomes. 2023; 21:116. doi:10.1186/s12955-023-02200-3.
Gomez-Cabello CA, Borna S, Pressman S, Haider SA, Haider CR, Forte AJ. Artificial-intelligence-based clinical decision support systems in primary care: a scoping review of current clinical implementations. Eur J Investig Health Psychol Educ. 2024; 14(3):685-698. doi:10.3390/ejihpe14030045.
Lin T, Zhang X, Gong J, et al. A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients. BMC Med Inform Decis Mak. 2023; 23(1):81. doi:10.1186/s12911-023-02175-7.
Wu M, Du X, Gu R, Wei J. Artificial intelligence for clinical decision support in sepsis. Front Med 2021; 8:665464. doi:10.3389/fmed.2021.665464.
Khalifa M, Albadawy M. Artificial intelligence for clinical prediction: exploring key domains and essential functions. Comput Methods Programs Biome Update. 2024; 100148. doi:10.1016/j.cmpbup.2024.100148.
Krishnan G, Singh S, Pathania M, et al. Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Front Artif Intell. 2023; 6:1227091. doi:10.3389/frai.2023.1227091.
Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023; 23(1):689. doi:org/10.1186/s12909-023-04698-z.
Khalifa M, Albadawy M. Ai in diagnostic imaging: revolutionising accuracy and efficiency. Comput Methods Programs Biomed Update. 2024; 5:100146. doi:10.1016/j.cmpbup.2024.100146.
Gala D, Behl H, Shah M, Makaryus A. The Role of artificial intelligence in improving patient outcomes and future of healthcare delivery in cardiology: a narrative review of the literature. Healthcare. 2024; 12(4):481. doi:10.3390/healthcare12040481.
Saeidnia HR, Firuzpour F, Kozak M, Majd HS. Advancing cancer diagnosis and treatment: integrating image analysis and ai algorithms for enhanced clinical practice. Artif Intell Rev. 2025; 58:105. doi:10.1007/s10462-025-11117-w.
Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019; 11(1):70. doi:10.1186/s13073-019-0689-8.
Rana M, Bhushan M. Machine learning and deep learning approach for medical image analysis: diagnosis to detection. Multimed Tools Appl. 2022; 24:1-39. doi:10.1007/s11042-022-14305-w.
Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021; 13(1):152. doi:10.1186/s13073-021-00968-x.
Ouanes K, Farhah N. Effectiveness of artificial intelligence (ai) in clinical decision support systems and care delivery. J Med Syst. 2024; 48(1):74. doi:10.1007/s10916-024-02098-4.
Braun M, Hummel P, Beck S, Dabrock P. Primer on an ethics of ai-based decision support systems in the clinic. J Med Ethics. 2020; 47(12):e3. doi:10.1136/medethics-2019-105860.
Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science. 1959; 130(3366):9–21. doi: 10.1126/science.130.3366.9.
Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res. 2022; 93(2):334-341. doi:10.1038/s41390-022-02226-1.
Labkoff S, Oladimeji B, Kannry J, et al. Toward a responsible future: recommendations for ai-enabled clinical decision support. J Am Med Inform Assoc. 2024; 31(11):2730-2739. doi:10.1093/jamia/ocae209.
Mehrolhassani MH, Behzadi A, Asadipour E. Key performance indicators in emergency department simulation: a scoping review. Scand J Trauma Resusc Emerg Med. 2025; 33(1):15. doi:10.1186/s13049-024-01318-7.
Austin EE, Blakely B, Tufanaru C, Selwood A, Braithwaite J, Clay-Williams R. Strategies to measure and improve emergency department performance: a scoping review. Scand J Trauma Resusc Emerg Med. 2020; 28(1):55. doi:10.1186/s13049-020-00749-2.
Novakovic A, Marshall AH. Introducing the DM-P approach for analysing the performances of real-time clinical decision support systems. Knowl Based Syst. 2020; 198:105877. doi:10.1016/j.knosys.2020.105877.
Moazemi S, Vahdati S, Li J, et al. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: a systematic review. Front Med. 2023; 10: 1109411. doi:10.3389/fmed.2023.1109411.
Alexiuk M, Elgubtan H, Tangri N. Clinical decision support tools in the electronic medical record. Kidney Int Rep. 2023; 9(1):29-38. doi.org/10.1016/j.ekir.2023.10.019.
Xie F, Zhou J, Lee J, et al. Benchmarking emergency department prediction models with machine learning and public electronic health records. Sci Data. 2022; 9(1):658.doi:10.1038/s41597-022-01782-9.
Iglesias CA, Favenza A, Carrera Á. A Big data reference architecture for emergency management. Information. 2020; 11(12):569. doi:10.3390/info11120569.
Chen Z, Liang N, Zhang H, et al. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart. 2023; 10(2): e002432. doi:10.1136/openhrt-2023-002432.
Berner ES, La Lande TJ. Overview of clinical decision support systems. In: Berner E, ed. Clinical Decision Support Systems. Cham, Switzerland: Springer; 2016:1-17. doi:10.1007/978-3-319-31913-1_1.
Lobach DF, Johns EB, Halpenny B, et al. Increasing complexity in rule-based clinical decision support: the symptom assessment and management ıntervention. JMIR Med Inform 2016; 4(4):e36. doi:10.2196/MEDINFORM.5728.
Papadopoulos P, Soflano M, Chaudy Y, Adejo W, Connolly T. A systematic review of technologies and standards used in the development of rule-based clinical decision support systems. Health Technol. 2022; 12:713-727. doi:org/10.1007/s12553-022-00672-9.
Masood A, Naseem U, Rashid J, Kim J, Razzak I. Review on enhancing clinical decision support system using machine learning. CAAI Trans Intell Technol. 2024; 1-14. doi:10.1049/cit2.12286.
Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS ONE. 2018; 13(7): e0201016. doi:10.1371/journal.pone.0201016.
Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA Jr, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019; 23(1):64. doi:10.1186/s13054-019-2351-7.
Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Markets. 2021; 31:685-695. doi:10.1007/s12525-021-00475-2.
Choi A, Choi SY, Chung K, et al. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department. Sci Rep. 2023; 13:8561. doi:10.1038/s41598-023-35617-3.
Han S, Choi W. Development of a large language model-based multi-agent clinical decision support system for korean triage and acuity scale (KTAS)-based triage and treatment planning in emergency departments. Adv Artif Intell Mach Learn. 2024; 5:3261-3275. doi:10.48550/arXiv.2408.07531.
Tyler S, Olis M, Aust N, et al. Use of artificial intelligence in triage in hospital emergency departments: a scoping review. Cureus. 2024; 16(5): e59906. doi:10.7759/cureus.59906.
Yasaka K, Abe O. Deep learning and artificial intelligence in radiology: current applications and future directions. PLoS Med. 15(11):e1002707. doi: 10.1371/journal.pmed.1002707.
Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023; 15(7):1916. doi:10.3390/pharmaceutics15071916.
Zhang PI, Hsu CC, Kao Y, et al. Real-time ai prediction for major adverse cardiac events in emergency department patients with chest pain. Scand J Trauma Resusc Emerg Med. 2020; 28(1):93. doi:10.1186/s13049-020-00786-x.