Hemşirelik Bakım Planında ChatGPT ve Gemini Kullanımının Karşılaştırılması: Meme Kanseri Örneği

Abstract

Amaç: Bu çalışmada, meme kanseri vakasında ChatGPT-3.5 ve Gemini-2.0 Flash’ın oluşturduğu hemşirelik bakım planının kalitesini değerlendirmek amaçlanmıştır. Gereç ve Yöntem: Çalışma tanımlayıcı ve karşılaştırmalı tiptedir. Çalışmada Türkiye’de hemşirelik sürecine ilişkin önemli kaynaklardan biri olan Birol’un Hemşirelik Süreci adlı kitabında meme kanseriyle ilgili bakım planı örneğindeki hasta anamnezi, yapay zeka modeli olan ChatGPT-3.5 ve Gemini-2.0 Flash’a tanıtılmıştır. Bu modellerin oluşturduğu bakım planı, Birol’un Hemşirelik Süreci adlı kitabındaki meme kanseriyle ilgili bakım planı örneğine ve Uluslararası Kuzey Amerika Hemşirelik Tanıları Birliği (NANDA) hemşirelik tanılarına göre değerlendirilmiştir. Bulgular: Yapay zeka modellerin hemşirelik bakım süreci aşamalarına verdiği yanıtlar değerlendirildiğinde her iki modelde de hemşirelik tanısı koyma, hedef belirleme, girişim planlama ve değerlendirme aşamaları varken veri toplama ve uygulama aşamalarını yalnızca ChatGPT-3.5 içermektedir. Örnek bakım planında yer alan 10 hemşirelik tanısından 7’si ChatGPT-3.5, 6’sı ise Gemini-2.0 Flash tarafından benzer şekilde oluşturulmuştur. Ayrıca her iki model, örnek bakım planında yer almayan ek tanılara da değinmiştir. ChatGPT-3.5 tarafından oluşturulan ek tanılar, NANDA'da mevcut tanılara benzerlik gösterirken, Gemini-2.0 Flash'nin oluşturduğu tanılar arasında NANDA’da yer almayan farklı tanılar da bulunmaktadır. Hemşirelik girişimleri açısından değerlendirildiğinde, ChatGPT-3.5, girişimlerin sonunda değerlendirme yaparak bakımın etkinliğini ölçmeye odaklanırken, Gemini-2.0 Flash’ın tüm girişimlerde hasta eğitimi ve bilgilendirme süreçlerine vurgu yaptığı belirlenmiştir. Sonuç: Hemşirelik sürecinde ChatGPT-3.5’in, Gemini-2.0 Flash’a kıyasla daha başarılı sonuçlar verdiği görülmüştür. Ayrıca her iki yapay zeka modelinin hemşirelik tanılamasında ve girişimlerinin belirlenmesinde hemşire kontrolünde kullanılabileceği belirlenmiştir. Bu doğrultuda, AI sistemlerin hemşirelik eğitim sürecine, hemşire eğitimcilerin denetimi ve rehberliğinde entegre edilmesi önerilmektedir.

Keywords

• ChatGPT
• gemini
• hemşirelik
• bakım

Comparison of Use of ChatGPT and Gemini in a Nursing Care Plan: The Case of Breast Cancer

Abstract

Objective: This study aimed to assess the quality of the nursing care plan developed using ChatGPT-3.5 and Gemini-2.0 Flash for a breast cancer patient. Material and Methods: The research was both descriptive and comparative. The patient's anamnesis in the breast cancer care plan example from Birol's Nursing Process, a significant resource on the nursing process in Turkey, was presented to the artificial intelligence models ChatGPT-3.5 and Gemini-2.0 Flash. The care plan formed by these models was assessed in relation to the breast cancer care plan example presented in Birol's book, Nursing Process, and the nursing diagnoses established by the International North American Nursing Diagnoses Association (NANDA). Results: Upon evaluating the replies of artificial intelligence models about the nursing care process, it was observed that both models encompassed the phases of nursing diagnosis, goal setting, intervention planning, and assessment; however, only ChatGPT-3.5 incorporated the stages of data collecting and execution. In the sample care plan, 7 nursing diagnoses were generated by ChatGPT-3.5, whereas 6 were produced by Gemini-2.0 Flash in a comparable manner. Furthermore, both models handled supplementary diagnoses that were excluded from the example care plan. The diagnoses generated by ChatGPT-3.5 were akin to those found in NANDA, but the diagnoses produced by Gemini-2.0 Flash encompassed distinct diagnoses absent from NANDA. In the context of nursing interventions, ChatGPT-3.5 concentrated on assessing the efficacy of care post-intervention, whereas Gemini-2.0 Flash prioritized patient education and informational processes during all interventions. Conclusion: ChatGPT-3.5 demonstrated superior efficacy compared to Gemini-2.0 Flash in the nursing process. Furthermore, it was established that both AI models are applicable in nursing diagnosis and the formulation of interventions under the supervision of nurses. It is advisable to incorporate AI systems into the nursing education process under the oversight of nursing educators.

Keywords

• ChatGPT
• gemini
• nursing
• care

References

1. . Pamela NZQ. The role of artificial intelligence in enhancing clinical nursing care: a scoping review. Journal of Nursing Management. 2021. doi: https://doi.org/10.1111/jonm.13425.
2. 2. Abdollahi R. Ethical challenges of the implementation of artificial intelligence in nursing care. Journal of Nursing Reports in Clinical Practice.2024:1-3.doi: https://doi.org/10.32598/JNRCP.2406.1125
3. 3. Mahmood YM, Mohammed RO, Habibullah IJ, Rahim HM, Salih AM. Comparing ChatGPT and Google Bard: Assessing AI-Powered Information Retrieval in Nursing. Barw Medical Journal. 2024. doi: https://doi.org/10.58742/hsn32c73
4. 4. Aminizadeh S, Heidari A, Dehghan M, Toumaj S, Rezaei M, Navimipour NJ, et al. Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artificial Intelligence in Medicine. 2024;149:102779.doi:https://doi.org/10.1016/j.artmed.2024.102779.
5. 5. Harkut DG, Kasat K. Introductory chapter: artificial intelligence-challenges and applications. Artificial Intelligence-Scope and Limitations. 2019. doi: 10.5772/intechopen.84624.
6. 6. Jutel M, Zemelka‐Wiacek M, Ordak M, Pfaar O, Eiwegger T, Rechenmacher M, et al. The artificial intelligence (AI) revolution: How important for scientific work and its reliable sharing. Allergy. 2023;78(8). doi: https://doi.org/10.1111/all.15778.
7. 7. Ruksakulpiwat S, Thorngthip S, Niyomyart A, Benjasirisan C, Phianhasin L, Aldossary H, et al. A systematic review of the application of artificial intelligence in nursing care: where are we, and what’s next? Journal of Multidisciplinary Healthcare. 2024:1603-16. doi: https://doi.org/10.2147/JMDH.S459946
8. 8. Martinez-Ortigosa A, Martinez-Granados A, Gil-Hernández E, Rodriguez-Arrastia M, Ropero-Padilla C, Roman P. Applications of artificial intelligence in nursing care: a systematic review. Journal of Nursing Management. 2023;2023(1):3219127. doi: https://doi.org/10.1155/2023/3219127.

9. 9. Carlà MM, Gambini G, Baldascino A, Boselli F, Giannuzzi F, Margollicci F, et al. Large language models as assistance for glaucoma surgical cases: a ChatGPT vs. Google Gemini comparison. Graefe's Archive for Clinical and Experimental Ophthalmology. 2024;262(9):2945-59.
10. 10. Sahin Ozdemir M, Ozdemir YE. Comparison of the performances between ChatGPT and Gemini in answering questions on viral hepatitis. Scientific Reports. 2025;15(1):1712.
11. 11. Khromchenko K, Shaikh S, Singh M, Vurture G, Rana RA, Baum JD, et al. ChatGPT-3.5 versus Google Bard: which large language model responds best to commonly asked pregnancy questions? Cureus. 2024;16(7). doi: https://doi.org/10.7759/cureus.65543.
12. 12. Malak Akgün B. Psikiyatri ve Ruh Sağlığı Hemşireliği Bakım Planı Oluştururken ChatGPT'yi Bir Destek Aracı Olarak Kullanmak. Yaşam Boyu Hemşirelik Dergisi. 2024;5(2):145-62.doi: https://doi.org/10.29228/llnursing.73035.
13. 13. Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in healthcare: Elsevier; 2020. p. 25-60.
14. 14. Joseph J, Moore ZE, Patton D, O'Connor T, Nugent LE. The impact of implementing speech recognition technology on the accuracy and efficiency (time to complete) clinical documentation by nurses: A systematic review. Journal of clinical nursing. 2020;29(13-14):2125-37. doi: https://doi.org/10.1111/jocn.15261
15. 15. Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, et al. Application scenarios for artificial intelligence in nursing care: rapid review. Journal of medical Internet research. 2021;23(11):e26522. doi: https://doi.org/doi:10.2196/26522.
16. 16. Penner SB, Mercado NR, Bernstein S, Erickson E, DuBois MA, Dreisbach C. Fostering Informed Consent and Shared Decision-Making in Maternity Nursing With the Advancement of Artificial Intelligence. MCN: The American Journal of Maternal/Child Nursing.2025;50(2):7885.doi:https://doi.org/10.1097/NMC.0000000000001083
17. 17. Karacan E. Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models. Journal of Nursing Care Quality. 2024;39(3):206-11. doi: https://doi.org/10.1097/NCQ.0000000000000766
18. 18. Açıkgöz G, Baykal Ü. Hemşirelerin mesleki rolleri ve özerkliğini destekleyen yasal düzenlemeler. İstanbul Kent University J Health Sciences. 2023;2(1):29-34.
19. 19. Hants L, Bail K, Paterson C. Clinical decision‐making and the nursing process in digital health systems: an integrated systematic review. Journal of clinical nursing. 2023;32(19-20):7010-35. doi: https://doi.org/10.1111/jocn.16823
20. 20. Shin JH, Jung SO, Lee JS. Identification of North American Nursing Diagnosis Association—Nursing Interventions Classification—Nursing Outcomes Classification of nursing home residents using on‐time data by android smartphone application by registered nurses. International journal of nursing knowledge. 2024;35(1):46-68. doi: https://doi.org/10.1111/2047-3095.12419.
21. 21. Yel P, Ozer S, Yildirim Y. Nursing Care Process οf a Patient with a Diagnosis of Systemic Lupus Erythamatosus: A Case Report. International Journal of Caring Sciences. 2024;17(3):1820.
22. 22. Martínez CEM, Eliska PV, Hilario SDV. Nursing care in postoperative patient of adnexal cyst in the obstetrics and gynecology service, in a national hospital of Calla. AG Salud. 2024;2:30-. doi: https://doi.org/10.62486/agsalud202430.
23. 23. Dinapoli L, Colloca G, Di Capua B, Valentini V. Psychological aspects to consider in breast cancer diagnosis and treatment. Current oncology reports.2021;23:1-7.doi: https://doi.org/10.1007/s11912-021-01049-3.
24. 24. Ke Q, Timmins F, Furlong E, Stokes D. Unmet care needs of women who have undergone breast cancer surgery: A scoping review. Journal of advanced nursing.2024;80(5):1732-49. doi: https://doi.org/10.1111/jan.15953.
25. 25. Tangi R, Solmaz R. Meme Kanseri Tanısında Wisconsin Veri Seti ile Makine Öğrenmesi Uygulamaları. Bilişim Teknolojileri Dergisi. 2025;18(1):29-43.
26. 26. Cruickshank S, Kennedy C, Lockhart K, Dosser I, Dallas L. Specialist breast care nurses for supportive care of women with breast cancer. Cochrane Database of Systematic Reviews. 2008(1). doi: https://doi.org/10.1002/14651858.
27. 27. Rodriguez‐Ortega A, Ferro T, Pérez X, Pla Farnos MJ, Gil‐Gil M, Lopez Ojeda A, et al. Self‐assessment of a breast care nursing model within a Breast Unit: learning process and keys to improving breast care. Journal of Clinical Nursing. 2023;32(11-12):2722-32. doi: https://doi.org/10.1111/jocn.16639.
28. 28. Herdman TH, Kamitsuru S, Lopes CT. NANDA International, Inc. Nursing Diagnoses: Definitions and Classification 2024-20262024.
29. 29. Birol L. Hemşirelik Süreci: Hemşirelik Bakımında Sistematik Yaklaşım. 10 ed. İzmir: Etki Matbacılık Yayıncılık; 2011.
30. 30. Liu HY, Alessandri Bonetti M, De Lorenzi F, Gimbel ML, Nguyen VT, Egro FM. Consulting the digital doctor: Google versus ChatGPT as sources of information on breast implant-associated anaplastic large cell lymphoma and breast implant illness. Aesthetic plastic surgery. 2024;48(4):590-607. doi: https://doi.org/10.1007/s00266-023-03713-4.
31. 31. Tepe M, Emekli E. Assessing the responses of large language models (ChatGPT-4, Gemini, and Microsoft Copilot) to frequently asked questions in breast imaging: a study on readability and accuracy.Cureus.2024;16(5).doi:https://doi.org/10.7759/cureus.59960.
32. 32. Mohammed SAAQ, Osman YMM, Ibrahim AM, Shaban M. Ethical and regulatory considerations in the use of AI and machine learning in nursing: A systematic review. International nursing review. 2025;72(1):e70010. doi: https://doi.org/10.1111/inr.70010.
33. 33. Rony MKK, Kayesh I, Bala SD, Akter F, Parvin MR. Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals-A descriptive qualitative study. Heliyon. 2024;10(4). doi: https://doi.org/10.1016/j.heliyon.2024.e25718.
34. 34. Castonguay A, Farthing P, Davies S, Vogelsang L, Kleib M, Risling T, et al. Revolutionizing nursing education through AI integration: A reflection on the disruptive impact of ChatGPT. Nurse education today.2023;129:105916.doi:https://doi.org/10.1016/j.nedt.2023.105916.
35. 35. Wong IH. Future and Challenges of Artificial Intelligence in Nursing Education. Macau Journal of Nursing. 2024;23(1). doi: 10.6729/MJN.202407_23(1).003.
36. 36. Schneidereith TA, Thibault J. The basics of artificial intelligence in nursing: Fundamentals and recommendations for educators. Journal of Nursing Education. 2023;62(12):716-20.doi: https://doi.org/10.3928/01484834-20231006-03.
37. 37. Ramírez‐Baraldes El, García‐Gutiérrez D, García‐Salido C. Artificial Intelligence in Nursing: New Opportunities and Challenges. European Journal of Education. 2025;60(1):e70033. doi: https://doi.org/10.1111/ejed.70033.