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Yoğun Bakımda Yapay Zekanın Etik Sonuçları: Klinik Karar Verme ve Hemşirelik Uygulamaları Üzerine Bir Literatür İncelemesi

Yıl 2025, Cilt: 29 Sayı: 2, 152 - 160, 16.08.2025
https://doi.org/10.62111/ybhd.1606594

Öz

Bu çalışma, yapay zeka destekli klinik karar verme süreçlerinin yoğun bakım ünitelerindeki (YBÜ) hemşirelik uygulamalarına etkilerini ve bu süreçlerin etik boyutlarını incelemektedir. Yapay zekanın sağlık sektöründeki hızlı ilerlemesi, hasta izleme, veri analizi ve klinik karar destek sistemleri gibi alanlarda önemli yenilikler getirmiştir. Destek verenler, yapay zekanın hasta sonuçlarını iyileştirebileceğini ve operasyonel verimliliği artırabileceğini savunmaktadır. Bununla birlikte, yapay zekanın hemşirelik uygulamalarına entegrasyonu, empati eksikliği ve hasta-hemşire ilişkisinin zayıflaması gibi etik sorunları da beraberinde getirmektedir. Yapay zeka, büyük verileri analiz ederek hızlı ve objektif kararlar alabilirken, hemşireler empati ve insani değerlere dayalı kararlar vermektedir. Yapay zekanın klinik karar verme süreçlerine entegrasyonu hemşirelerin yeteneklerini artırabilmekte, ancak aynı zamanda etik ikilemler de yaratmaktadır. Yapay zeka kullanımındaki hatalardan kimin sorumlu olduğu sorusu önemli bir etik sorun teşkil etmektedir. Buna ek olarak, yapay zekanın büyük veri setlerini analiz etmesi, hasta verilerinin gizliliği konusunda endişelere yol açmaktadır. Bir başka endişe kaynağı da yapay zekanın empati ve insani dokunuş gibi hemşirelik bakımında kritik olan niteliklerden yoksun olmasıdır. Sonuç olarak, yapay zeka ve hemşire işbirliği hasta bakımını iyileştirme potansiyeline sahiptir, ancak bu teknolojinin entegrasyonu için etik sorumluluklar ve hesap verebilirlik gibi konuların dikkatlice ele alınması gerekmektedir.

Kaynakça

  • 1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
  • 2. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30-36.
  • 3. Fogel AL, Kvedar JC. Artificial intelligence powers digital medicine. NPJ Digit Med. 2018;1(1):5.
  • 4. Harry A. The future of medicine: Harnessing the power of aI for revolutionizing healthcare. Int J Multidiscip Sci Arts. 2023;2(1):36-47.
  • 5. Ntoutsi E, Fafalios P, Gadiraju U, et al. Bias in data-driven artificial intelligence systems—An introductory survey. Wiley Interdiscip Rev Data Min Knowl Discov. 2020;10(3):e1356.
  • 6. Reynolds W. Empathy: The core of the nurse-client relationship. From Ther Relationships to Transitional Care A Theor Pract Roadmap. Published online January 2021:61-66.
  • 7. Char DS, Shah NH, Magnus D. Implementing machine learning in health care — Addressing ethical challenges. N Engl J Med. 2018;378(11):981-983.
  • 8. Nguyen D, Ngo B, vanSonnenberg E. AI in the intensive care unit: Up-to-Date review. J Intensive Care Med. 2021;36(10):1115-1123.
  • 9. Peter E, Liaschenko J. Moral distress reexamined: A feminist interpretation of nurses’ identities, relationships, and responsibilites. J Bioeth Inq. 2013;10(3):337-345.
  • 10. Maharmeh M, Alasad J, Salami I, Saleh Z, Darawad M. Clinical decision-making among critical care nurses: A qualitative study. Health (Irvine Calif). 2016;08(15):1807-1819.
  • 11. Maharmeh M. Understanding critical care nurses’ autonomy in Jordan. Leadersh Heal Serv. 2017;30(4):432-442.
  • 12. Pepito JA, Locsin R. Can nurses remain relevant in a technologically advanced future? Int J Nurs Sci. 2019;6(1):106-110.
  • 13. Mamdani M, Slutsky AS. Artificial intelligence in intensive care medicine. Intensive Care Med. 2021;47(2):147-149.
  • 14. Ng ZQP, Ling LYJ, Chew HSJ, Lau Y. The role of artificial intelligence in enhancing clinical nursing care: A scoping review. J Nurs Manag. 2022;30(8):3654-3674.
  • 15. Martinez-Ortigosa A, Martinez-Granados A, Gil-Hernández E, Rodriguez-Arrastia M, Ropero-Padilla C, Roman P. Applications of artificial ıntelligence in nursing care: A systematic review. J Nurs Manag. 2023:3219127.
  • 16. Chen Y, Moreira P, Liu W wei, Monachino M, Nguyen TLH, Wang A. Is there a gap between artificial intelligence applications and priorities in health care and nursing management? J Nurs Manag. 2022;30(8):3736-3742.
  • 17. Ghane G, Ghiyasvandian S, Chekeni AM, Karimi R. Revolutionizing nursing education and care: The role of artificial intelligence in nursing. Nurse Author Ed. 2024;34(1):e12057.
  • 18. Tabudlo J, Kuan L, Garma PF. Can nurses in clinical practice ascribe responsibility to intelligent robots? Nurs Ethics. 2022;29(6):1457-1465.
  • 19. Yadav S. Embracing artificial intelligence: Revolutionizing nursing documentation for a better future. Cureus. 2024;16(4):e57725.
  • 20. Higgins O, Chalup SK, Wilson RL. Artificial Intelligence in nursing: trustworthy or reliable? J Res Nurs. 2024;29(2):143-153.
  • 21. Khalaf AK, Ghallab SA, Abd Elhafez KH. Health care providers’ perception about artificial intelligence applications. Assiut Sci Nurs J. 2022;10(31):204-215.
  • 22. Johnson EA, Dudding KM, Carrington JM. When to err is inhuman: An examination of the influence of artificial intelligence-driven nursing care on patient safety. Nurs Inq. 2024;31(1) e12583.
  • 23. Ronquillo CE, Peltonen LM, Pruinelli L, et al. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the nursing and artificial intelligence leadership collaborative. J Adv Nurs. 2021;77(9):3707-3717.
  • 24. Chang CY, Jen HJ, Su WS. Trends in artificial intelligence in nursing: Impacts on nursing management. J Nurs Manag. 2022;30(8):3644-3653.
  • 25. Abuzaid MM, Elshami W, Fadden SM. Integration of artificial intelligence into nursing practice. Health Technol (Berl). 2022;12(6):1109-1115.
  • 26. Meng L, Tomiyama H, Saho K, et al. Managing Security of Healthcare Data for a Modern Healthcare System. Sensors. 2023;23(7):3612.
  • 27. Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput Biol Med. 2023;158:106848.
  • 28. Firdous Turak S, Jadhao A, Makhijani S. AI Used in Healthcare and Data Privacy. 2024 2nd DMIHER Int Conf Artif Intell Heal Educ Ind IDICAIEI. 2024:1-5.
  • 29. Mohanasundari SK, Kalpana M, Madhusudhan U, et al. Can Artificial Intelligence Replace the Unique Nursing Role? Cureus. 2023;5(12):e51150.
  • 30. Grezenko H, Alsadoun L, Farrukh A, et al. From Nanobots to Neural Networks: Multifaceted Revolution of Artificial Intelligence in Surgical Medicine and Therapeutics. Cureus. 2023;15(11):e49082.
  • 31. Bar-Tur L, Inbal-Jacobson M, Brik-Deshen S, Zilbershlag Y, Pearl Naim S, Brick Y. Telephone-Based Emotional Support for Older Adults during the COVID-19 Pandemic. J Aging Soc Policy. 2021;33(4-5):522-538.
  • 32. Edmonds C, Lockwood GM, Bezjak A, Nyhof-Young J. Alleviating emotional exhaustion in oncology nurses: An evaluation of wellspring’s “care for the professional caregiver program.” J Cancer Educ. 2012;27(1):27-36.
  • 33. Khosla R, Chu MT. Embodying care in matilda: An affective communication robot for emotional wellbeing of older people in Australian residential care facilities. ACM Trans Manag Inf Syst. 2013;4(4):1-33.
  • 34. Ara A, Farjana Mifa A. Integrating artifıcial intelligence and big data in mobile health: A systematic revıew of innovations and challenges in healthcare systems. Glob Mainstream J Business, Econ Dev Proj Manag. 2024;3(01):01-16.
  • 35. Elendu C, Amaechi DC, Elendu TC, et al. Ethical implications of AI and robotics in healthcare: A review. Med (United States). 2023;102(50):E36671.
  • 36. Arigbabu AT, Olaniyi OO, Adigwe CS, Adebiyi OO, Ajayi SA. Data governance in aI - enabled healthcare systems: A case of the project nightingale. Asian J Res Comput Sci. 2024;17(5):85-107.
  • 37. Saheb T, Saheb T, Carpenter DO. Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis. Comput Biol Med. 2021;135.
  • 38. Williamson SM, Prybutok V. Balancing privacy and P,progress: A review of privacy challenges, systemic oversight, and patient perceptions in aI-driven healthcare. Appl Sci. 2024;14(2):675.
  • 39. Guan J. Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance. Chinese Med Sci J. 2019;34(2):76-83.
  • 40. Biller-Andorno N, Ferrario A, Joebges S, et al. AI support for ethical decision-making around resuscitation: Proceed with care. J Med Ethics. 2021;48(3):175-183.
  • 41. Shillan D, Sterne JAC, Champneys A, Gibbison B. Use of machine learning to analyse routinely collected intensive care unit data: A systematic review. Crit Care. 2019;23(1):284.
  • 42. Do Nascimento IJB, Marcolino MS, Abdulazeem HM, et al. Impact of big data analytics on people’s health: Overview of systematic reviews and recommendations for future studies. J Med Internet Res. 2021; 23(4):e27275.
  • 43. Malik A, Kumar S, Basu S, Bebenroth R. Managing disruptive technologies for innovative healthcare solutions: The role of high-involvement work systems and technologically-mediated relational coordination. J Bus Res. 2023;161.
  • 44. Huang K, Jiao Z, Cai Y, Zhong Z. Artificial intelligence-based intelligent surveillance for reducing nurses’ working hours in nurse–patient interaction: A two-wave study. J Nurs Manag. 2022;30(8):3817-3826.
  • 45. Li J, Zhou L, Zhan Y, et al. How does the artificial intelligence-based image-assisted technique help physicians in diagnosis of pulmonary adenocarcinoma? A randomized controlled experiment of multicenter physicians in China. J Am Med Informatics Assoc. 2022;29(12):2041-2049.
  • 46. Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: Systematic literature review. JMIR Med Informatics. 2020;8(7):e18599.
  • 47. Adegboro CO, Choudhury A, Asan O, Kelly MM. Artificial intelligence to improve health outcomes in the NICU and PICU: A systematic review. Hosp Pediatr. 2022;12(1):93-107.

Ethical Implications of AI in Intensive Care: A Literature Review of Clinical Decision-Making and Nursing Practices

Yıl 2025, Cilt: 29 Sayı: 2, 152 - 160, 16.08.2025
https://doi.org/10.62111/ybhd.1606594

Öz

This study examines the effects of artificial intelligence (AI)-assisted clinical decision-making processes on nursing practice in intensive care units (ICU) and the ethical dimensions of these processes. The rapid advancement of AI in the healthcare sector has introduced significant innovations in areas such as patient monitoring, data analysis and clinical decision support systems. Supporters argue that AI can improve patient outcomes and increase operational efficiency. However, the integration of AI into nursing practice brings ethical issues, such as a lack of empathy and weakening of the patient-nurse relationship. While AI can make fast and objective decisions by analysing big data, nurses make decisions based on empathy and human values. The integration of AI into clinical decision-making processes can increase nurses’ capabilities, but it also creates ethical dilemmas. The question of who is responsible for errors in AI use poses an important ethical issue. In addition, AI’s analysis of large data sets raises concerns about the confidentiality of patient data. Another source of concern is that AI lacks qualities that are critical in nursing care, such as empathy and human touch. In conclusion, AI and nurse collaboration has the potential to improve patient care, but issues such as ethical responsibilities and accountability for the integration of this technology need to be carefully addressed.

Kaynakça

  • 1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
  • 2. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30-36.
  • 3. Fogel AL, Kvedar JC. Artificial intelligence powers digital medicine. NPJ Digit Med. 2018;1(1):5.
  • 4. Harry A. The future of medicine: Harnessing the power of aI for revolutionizing healthcare. Int J Multidiscip Sci Arts. 2023;2(1):36-47.
  • 5. Ntoutsi E, Fafalios P, Gadiraju U, et al. Bias in data-driven artificial intelligence systems—An introductory survey. Wiley Interdiscip Rev Data Min Knowl Discov. 2020;10(3):e1356.
  • 6. Reynolds W. Empathy: The core of the nurse-client relationship. From Ther Relationships to Transitional Care A Theor Pract Roadmap. Published online January 2021:61-66.
  • 7. Char DS, Shah NH, Magnus D. Implementing machine learning in health care — Addressing ethical challenges. N Engl J Med. 2018;378(11):981-983.
  • 8. Nguyen D, Ngo B, vanSonnenberg E. AI in the intensive care unit: Up-to-Date review. J Intensive Care Med. 2021;36(10):1115-1123.
  • 9. Peter E, Liaschenko J. Moral distress reexamined: A feminist interpretation of nurses’ identities, relationships, and responsibilites. J Bioeth Inq. 2013;10(3):337-345.
  • 10. Maharmeh M, Alasad J, Salami I, Saleh Z, Darawad M. Clinical decision-making among critical care nurses: A qualitative study. Health (Irvine Calif). 2016;08(15):1807-1819.
  • 11. Maharmeh M. Understanding critical care nurses’ autonomy in Jordan. Leadersh Heal Serv. 2017;30(4):432-442.
  • 12. Pepito JA, Locsin R. Can nurses remain relevant in a technologically advanced future? Int J Nurs Sci. 2019;6(1):106-110.
  • 13. Mamdani M, Slutsky AS. Artificial intelligence in intensive care medicine. Intensive Care Med. 2021;47(2):147-149.
  • 14. Ng ZQP, Ling LYJ, Chew HSJ, Lau Y. The role of artificial intelligence in enhancing clinical nursing care: A scoping review. J Nurs Manag. 2022;30(8):3654-3674.
  • 15. Martinez-Ortigosa A, Martinez-Granados A, Gil-Hernández E, Rodriguez-Arrastia M, Ropero-Padilla C, Roman P. Applications of artificial ıntelligence in nursing care: A systematic review. J Nurs Manag. 2023:3219127.
  • 16. Chen Y, Moreira P, Liu W wei, Monachino M, Nguyen TLH, Wang A. Is there a gap between artificial intelligence applications and priorities in health care and nursing management? J Nurs Manag. 2022;30(8):3736-3742.
  • 17. Ghane G, Ghiyasvandian S, Chekeni AM, Karimi R. Revolutionizing nursing education and care: The role of artificial intelligence in nursing. Nurse Author Ed. 2024;34(1):e12057.
  • 18. Tabudlo J, Kuan L, Garma PF. Can nurses in clinical practice ascribe responsibility to intelligent robots? Nurs Ethics. 2022;29(6):1457-1465.
  • 19. Yadav S. Embracing artificial intelligence: Revolutionizing nursing documentation for a better future. Cureus. 2024;16(4):e57725.
  • 20. Higgins O, Chalup SK, Wilson RL. Artificial Intelligence in nursing: trustworthy or reliable? J Res Nurs. 2024;29(2):143-153.
  • 21. Khalaf AK, Ghallab SA, Abd Elhafez KH. Health care providers’ perception about artificial intelligence applications. Assiut Sci Nurs J. 2022;10(31):204-215.
  • 22. Johnson EA, Dudding KM, Carrington JM. When to err is inhuman: An examination of the influence of artificial intelligence-driven nursing care on patient safety. Nurs Inq. 2024;31(1) e12583.
  • 23. Ronquillo CE, Peltonen LM, Pruinelli L, et al. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the nursing and artificial intelligence leadership collaborative. J Adv Nurs. 2021;77(9):3707-3717.
  • 24. Chang CY, Jen HJ, Su WS. Trends in artificial intelligence in nursing: Impacts on nursing management. J Nurs Manag. 2022;30(8):3644-3653.
  • 25. Abuzaid MM, Elshami W, Fadden SM. Integration of artificial intelligence into nursing practice. Health Technol (Berl). 2022;12(6):1109-1115.
  • 26. Meng L, Tomiyama H, Saho K, et al. Managing Security of Healthcare Data for a Modern Healthcare System. Sensors. 2023;23(7):3612.
  • 27. Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput Biol Med. 2023;158:106848.
  • 28. Firdous Turak S, Jadhao A, Makhijani S. AI Used in Healthcare and Data Privacy. 2024 2nd DMIHER Int Conf Artif Intell Heal Educ Ind IDICAIEI. 2024:1-5.
  • 29. Mohanasundari SK, Kalpana M, Madhusudhan U, et al. Can Artificial Intelligence Replace the Unique Nursing Role? Cureus. 2023;5(12):e51150.
  • 30. Grezenko H, Alsadoun L, Farrukh A, et al. From Nanobots to Neural Networks: Multifaceted Revolution of Artificial Intelligence in Surgical Medicine and Therapeutics. Cureus. 2023;15(11):e49082.
  • 31. Bar-Tur L, Inbal-Jacobson M, Brik-Deshen S, Zilbershlag Y, Pearl Naim S, Brick Y. Telephone-Based Emotional Support for Older Adults during the COVID-19 Pandemic. J Aging Soc Policy. 2021;33(4-5):522-538.
  • 32. Edmonds C, Lockwood GM, Bezjak A, Nyhof-Young J. Alleviating emotional exhaustion in oncology nurses: An evaluation of wellspring’s “care for the professional caregiver program.” J Cancer Educ. 2012;27(1):27-36.
  • 33. Khosla R, Chu MT. Embodying care in matilda: An affective communication robot for emotional wellbeing of older people in Australian residential care facilities. ACM Trans Manag Inf Syst. 2013;4(4):1-33.
  • 34. Ara A, Farjana Mifa A. Integrating artifıcial intelligence and big data in mobile health: A systematic revıew of innovations and challenges in healthcare systems. Glob Mainstream J Business, Econ Dev Proj Manag. 2024;3(01):01-16.
  • 35. Elendu C, Amaechi DC, Elendu TC, et al. Ethical implications of AI and robotics in healthcare: A review. Med (United States). 2023;102(50):E36671.
  • 36. Arigbabu AT, Olaniyi OO, Adigwe CS, Adebiyi OO, Ajayi SA. Data governance in aI - enabled healthcare systems: A case of the project nightingale. Asian J Res Comput Sci. 2024;17(5):85-107.
  • 37. Saheb T, Saheb T, Carpenter DO. Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis. Comput Biol Med. 2021;135.
  • 38. Williamson SM, Prybutok V. Balancing privacy and P,progress: A review of privacy challenges, systemic oversight, and patient perceptions in aI-driven healthcare. Appl Sci. 2024;14(2):675.
  • 39. Guan J. Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance. Chinese Med Sci J. 2019;34(2):76-83.
  • 40. Biller-Andorno N, Ferrario A, Joebges S, et al. AI support for ethical decision-making around resuscitation: Proceed with care. J Med Ethics. 2021;48(3):175-183.
  • 41. Shillan D, Sterne JAC, Champneys A, Gibbison B. Use of machine learning to analyse routinely collected intensive care unit data: A systematic review. Crit Care. 2019;23(1):284.
  • 42. Do Nascimento IJB, Marcolino MS, Abdulazeem HM, et al. Impact of big data analytics on people’s health: Overview of systematic reviews and recommendations for future studies. J Med Internet Res. 2021; 23(4):e27275.
  • 43. Malik A, Kumar S, Basu S, Bebenroth R. Managing disruptive technologies for innovative healthcare solutions: The role of high-involvement work systems and technologically-mediated relational coordination. J Bus Res. 2023;161.
  • 44. Huang K, Jiao Z, Cai Y, Zhong Z. Artificial intelligence-based intelligent surveillance for reducing nurses’ working hours in nurse–patient interaction: A two-wave study. J Nurs Manag. 2022;30(8):3817-3826.
  • 45. Li J, Zhou L, Zhan Y, et al. How does the artificial intelligence-based image-assisted technique help physicians in diagnosis of pulmonary adenocarcinoma? A randomized controlled experiment of multicenter physicians in China. J Am Med Informatics Assoc. 2022;29(12):2041-2049.
  • 46. Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: Systematic literature review. JMIR Med Informatics. 2020;8(7):e18599.
  • 47. Adegboro CO, Choudhury A, Asan O, Kelly MM. Artificial intelligence to improve health outcomes in the NICU and PICU: A systematic review. Hosp Pediatr. 2022;12(1):93-107.
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Dahili Hastalıklar Hemşireliği
Bölüm Derleme
Yazarlar

Ahmet Ceviz 0009-0004-3536-2113

Gürkan Özden 0000-0002-2775-3163

Gönderilme Tarihi 26 Aralık 2024
Kabul Tarihi 25 Temmuz 2025
Yayımlanma Tarihi 16 Ağustos 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 29 Sayı: 2

Kaynak Göster

Bu derginin içeriği Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı kapsamında lisanslanmıştır.

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