Responses and accuracy of artificial intelligence systems to prompts on pressure injury management

Authors

Keywords:

Generative Artificial Intelligence, Large Language Models, Nursing, Team, Clinical Reasoning, Pressure Ulcer

Abstract

Objective: To compare the responses and accuracy of different Artificial Intelligences based on a set of prompts for the management of pressure injury.

Method: A descriptive, comparative study of the responses of Artificial Intelligences, carried out between October and November 2025, in two stages: 1) Study of prompts and 2) Test to obtain the responses. The following were evaluated: Perplexity AI, Grok AI, Blackbox AI, OpenAI ChatGPT-4.0®, Gemini®, and Claude AI, randomly divided into the hybrid prompting and zero-shot groups. The responses were organized in an Excel spreadsheet and analyzed descriptively.

Results: The overall accuracy of the artificial intelligences was 31.9%. The hybrid prompting group reached 36.1% and the zero-shot obtained 27.7%. The Gemini® AI demonstrated the highest accuracy and ChatGPT-4.0® the worst.

Conclusion: The accuracy presented indicates that the safest recommendation is the hybrid prompt model, integrating artificial intelligence with human clinical judgment.

Author Biographies

Letícia Pereira Pires, Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Nurse. Bachelor's degree in Nursing from the Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Maithê de Carvalho e Lemos Goulart, Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Nurse. PhD in Sciences. Professor at the Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Fernanda Garcia Bezerra Góes, Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Nurse. PhD in Nursing. Professor at the Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Yonara Cristiane Ribeiro, Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Nurse. PhD in Sciences. Professor at the Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Luíza Lissonger Costa Guimarães, Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Nurse. Bachelor's degree in Nursing from the Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Leidiane Farias de Sousa, Federal Fluminense University, Rio das Ostras, RJ, Brazil.

Undergraduate student in Nursing at the Federal Fluminense University, Rio das Ostras, RJ, Brazil.

References

1. Pott FS, Meier MJ, Stocco JGD, Petz FFC, Roehrs H, Ziegelmann PK. Pressure injury prevention measures: overview of systematic reviews. Rev Esc Enferm USP [Internet]. 2023 [cited 2025 Aug 15];57:e20230039. Available from: https://doi.org/10.1590/1980-220X-REEUSP-2023-0039en

2. Fontes TLA, de Oliveira BGRB, De Oliveira MF, da Silva MA, da Silva ARG, Pires BMFB, et al. Development of a checklist to prevent pressure injuries in patients with COVID-19. Rev enferm UFPE on line [Internet]. 2024 [cited 2025 Aug 15];18(1):e257602. Available from: https://doi.org/10.5205/1981-8963.2024.257602

3. Macêdo SM, Bastos LLAG, Oliveira RGC, Lima MCV, Gomes FCF. Selection criteria for primary dressings in the treatment of pressure ulcers in hospitalized patients. Cogitare Enferm [Internet]. 2021 [cited 2025 Aug 25];26:e74400. Available from: http://dx.doi.org/10.5380/ce.v26i0.74400

4. de Araújo CAF, Pereira SRM, de Paula VG, de Oliveira JA, de Andrade KBS, de Oliveira NVD. Evaluation of the knowledge of nursing professionals in the prevention of pressure ulcer in intensive care. Esc Anna Nery [Internet]. 2022 [cited 2025 Aug 25];26:e20210200. Available from: https://doi.org/10.1590/2177-9465-EAN-2021-0200

5. Kotp MH, Ismail HA, Basyouny HAA, Aly MA, Hendy A, Nashwan AJ, et al. Empowering nurse leaders: readiness for AI integration and the perceived benefits of predictive analytics. BMC Nursing [Internet]. 2025 [cited 2025 Aug 25];24(56):1-13. Available from: https://doi.org/10.1186/s12912-024-02653-x

6. Dal Sasso GTM, Lanzoni GMM, Alvarez AG, Barra DCC, Barbosa SFF. Potential contribution of Chatgpt® to learning about septic shock in intensive care. Texto Contexto Enferm [Internet]. 2024 [cited 2025 Aug 25];33:e20230184. Available from: https://doi.org/10.1590/1980-265X-TCE-2023-0184en

7. Vitorino LM, Yoshinari Junior GH, Lopes-Júnior LC. Artificial intelligence in nursing: advancing clinical judgment and decision-making. Rev Bras Enferm [Internet]. 2025 [cited 2025 Nov 19];78(4):e780401. Available from: https://doi.org/10.1590/0034-7167.2025780401

8. 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. J Med Internet Res [Internet]. 2021 [cited 2025 Aug 22];23(11):e26522. Available from: https://www.jmir.org/2021/11/e26522

9. Sampaio RC, Sabbatini M, Limongi R. Diretrizes para o uso ético e responsável da Inteligência Artificial Generativa: um guia prático para pesquisadores. São Paulo: Editora Intercom [Internet]. 2024 [cited 2025 Aug 20]. 62 p. Available from: https://prpg.unicamp.br/noticias/lancamento-diretrizes-para-o-uso-etico-e-responsavel-da-inteligencia-artificial-generativa-um-guia-pratico-para-pesquisadores/

10. Shi J, Chen H, Shi C, Su C, Yue S, Li W, et al. Development and comparative evaluation of knowledge graph-enhanced large language models for domain-specific question answering in nursing. BMC Nursing [Internet]. 2026 [cited 2026 May 5];25:530. Available from: https://doi.org/10.1186/s12912-026-04647-3

11. Gurler N, Sengul T, Bulbul SH, Akyaz DY, Guler O, Yantac AE, et al. Secure and accessible AI in nursing education: a modular agentic chatbot framework comparing ChatGPT-4o with an open-source LLM for chronic wound care. Nurse Educ Pract [Internet]. 2026 [cited 2026 May 5];93:104789. Available from: https://doi.org/10.1016/j.nepr.2026.104789

12. Bsharat SM, Myrzakhan A, Shen Z. Principled instructions are all you need for questioning LLaMA-1/2, GPT-3.5/4. arXiv [Internet]. 2024 [cited 2025 Sep 19];2:1-26. Available from: https://arxiv.org/html/2312.16171v2

13. Pan American Health Organization (PAHO). AI Prompt design for public health: using generative AI responsibly [Internet]. OPAS; 2025. [cited 2025 Sep 30]. 61 p. Available from: https://iris.paho.org/items/b98d9c0f-6008-4b78-9066-01e1e81c29c5

14. Schulhouff S, Llie M, Balepur N, Kahadze K, Liu A, Si C, et al. The prompt report: a systematic survey of prompt engineering techniques. arXiv [Internet]. 2024 [cited 2025 Sep 30];6:1-80. Available from: https://arxiv.org/abs/2406.06608

15. Vilakati, S. Prompt engineering for accurate statistical reasoning with large language models in medical research. Front Artif Intell [Internet]. 2025 [cited 2025 Nov 18];8:1658316. Available from: https://doi.org/10.3389/frai.2025.1658316

16. Dhuliawala S, Komeili M, Xu J, Raileanu R, Li X, Celikyilmaz, et al. Chain-of-verification reduces hallucination in large language models. arXiv [Internet]. 2023 [cited 2025 Oct 12];2:1-19. Available from: https://arxiv.org/abs/2309.11495

17. Bernardes, RM. Prevenção e manejo da lesão por pressão: segurança do paciente [Internet]. São Paulo: Universidade de São Paulo; 2020 [cited 2025 Sep 15]. Available from: http://eerp.usp.br/feridascronicas/recurso_educacional_lp_1_4.html

18. National Pressure Injury Advisory Panel (NPIAP). Prevention and treatment of pressure ulcers/injuries: the international guideline [Internet]. Washington; 2019 [cited 2025 Aug 15]. 405 p. Available from: https://static1.squarespace.com/static/6479484083027f25a6246fcb/t/6553d3440e18d57a550c4e7e/1699992399539/CPG2019edition-digital-Nov2023version.pdf

19. Potter PA, Perry AG, Stockert PS. Fundamentos de enfermagem. 11th. Rio de Janeiro: Guanabara Koogan; 2024. 1644 p.

20. Zhang C, Zhang S, Wu B, Zou K, Chen H. Efficacy of different types of dressings on pressure injuries: systematic review and network meta-analysis. Nurs Open [Internet]. 2023 [cited 2025 Aug 10];10(9):5857-67. Available from: https://doi.org/10.1002/nop2.1867

21. Holman, M. Using tap water compared with normal saline for cleansing wounds in adults: a literature review of the evidence. J Wound Care [Internet]. 2023 [cited 2025 Oct 10];32(8):507-12. Available from: https://doi.org/10.12968/jowc.2023.32.8.507

22. dos Santos TO, da Silva JVL, Rocha RG, Assad LG, da Costa CCP, Pires BMFB. Papain with urea cream in pressure injuries: a case series study. Rev Enferm Atenção Saúde [Internet]. 2024 [cited 2025 Oct 10];13(1):e202404. Available from: https://seer.uftm.edu.br/revistaeletronica/index.php/enfer/article/view/6950

23. O'Connor S, Peltonen LM, Topaz M, Chen LYA, Michalowski M, Ronquillo C, et al. Prompt engineering when using generative AI in nursing education. Nurse Educ Pract [Internet]. 2024 [cited 2025 Nov 19];74:103825. Available from: https://doi.org/10.1016/j.nepr.2023.103825

24. Wang MH, Jiang X, Zeng P, Li X, Chong KKL, Hou G, et al. Balancing accuracy and user satisfaction: the role of prompt engineering in AI-driven healthcare solutions. Front Artif Intell [Internet]. 2025 [cited 2025 Oct 19];8:1517918. Available from: https://doi.org/10.3389/frai.2025.1517918

25. Wang M, Cui J, Lee SMY, Lin Z, Zeng P, Li X, et al. Applied machine learning in intelligent systems: knowledge graph-enhanced ophthalmic contrastive learning with “clinical profile” prompts. Front Artif Intell [Internet]. 2025 [cited 2025 Oct 19];8:1527010. Available from: https://doi.org/10.3389/frai.2025.1527010

26. Flemyng E, Noel-Storr A, Macura B, Gartlehner G, Thomas J, Meerpohl JJ, et al. Position statement on artificial intelligence (AI) use in evidence synthesis across Cochrane, the Campbell Collaboration, JBI, and the collaboration for environmental evidence 2025. JBI Evid Synth [Intermet]. 2025 [cited 2025 Oct 10];23(11):2162-6. Available from: https://doi.org/10.11124/JBIES-25-00480

27. Krifors A, Beskow T, Jonsson M, Lindner KJ, Calås J, Arwsbo V, et al. Improving medication error classification using a reasoning large language model. JAMIA Open [Internet]. 2026 [cited 2025 May 5];9(1):ooag004. Available from: https://doi.org/10.1093/jamiaopen/ooag004

28. Kim SK, Kim GM, Cha S, Jung M. Verification of the validity and reliability of therapeutic communication responses generated by large language models (LLMs): a comparative study of prompt-engineering strategies. J Korean Acad Psychiatr Ment Health Nurs [Internet]. 2025 [cited 2025 May 5];34(Spec):23-35. Available from: https://doi.org/10.12934/jkpmhn.2025.34.S1.23

29. Klein A, Ayoub N, Juhel C, Schuller R, Armstrong F, Pegalajar-Jurado A. Performance of a gelling fibre dressing in management of wounds in a community setting: a sub-analysis of the VIPES study. J Wound Care [Internet]. 2024 [cited 2025 Nov 19];33(7):464-73. Available from: https://doi.org/10.12968/jowc.2024.0125

30. Kimiafar K, Sarbaz M, Tabatabei SM, Ghaddaripouri K, Mousavi AS, Raei M, et al. Artificial intelligence literacy among healthcare professionals and students: a systematic review. Front Health Inform [Internet]. 2023 [cited 2026 May 5];12:168. Available from: https://www.researchgate.net/publication/375600294_Artificial_Intelligence_Literacy_Among_Healthcare_Professionals_and_Students_A_Systematic_Review

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Published

2026-09-09

How to Cite

Pires, L. P., Goulart, M. de C. e L., Góes, F. G. B., Ribeiro, Y. C., Guimarães, L. L. C., & de Sousa, L. F. (2026). Responses and accuracy of artificial intelligence systems to prompts on pressure injury management. Cogitare Enfermagem, 31. Retrieved from https://revistas.ufpr.br/cogitare/article/view/102398

Issue

Vol. 31 (2026)

Section

Original Articles

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Copyright (c) 2026 Letícia Pereira Pires, Maithê de Carvalho e Lemos Goulart, Fernanda Garcia Bezerra Góes, Yonara Cristiane Ribeiro, Luíza Lissonger Costa Guimarães, Leidiane Farias de Sousa

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