Una revisione dell'ambito sull'intersezione tra intelligenza artificiale (AI) e infermieristica opportunità, sfide e direzioni future

Contenuto principale dell'articolo

Giuseppe Zingaro
Mariangela Vacca
Francesca Spina
Maria Valeria Massida
Roberta Rosmarino
Ingrid Dallana Avilez Gonzalez
Ronald Jaimes Fuentes
Maria Rita Pinna
Maria Orsola Pisu
Cesar Ivan Aviles Gonzalez


L'intelligenza artificiale (AI) ha assistito a un'evoluzione impressionante negli ultimi anni, con conseguenti applicazioni innovative in vari settori, tra cui l'assistenza sanitaria (Davenport & Kalakota, 2019).

L'integrazione di questa tecnologia nella pratica infermieristica richiede un'esplorazione rigorosa per migliorare la precisione, l'efficienza e l'assistenza personalizzata. La letteratura attuale mostra un crescente interesse per questa intersezione, con prove preliminari che dimostrano sia opportunità significative che sfide notevoli (Topol, 2019).


I dati di download non sono ancora disponibili

Dettagli dell'articolo

Come citare
Zingaro, G., Vacca, M., Spina, F., Massida, M.V., Rosmarino, R., Gonzalez, I.D.A., Fuentes, R.J., Pinna, M.R., Pisu, M.O. e Gonzalez, C.I.A. 2023. Una revisione dell’ambito sull’intersezione tra intelligenza artificiale (AI) e infermieristica: opportunità, sfide e direzioni future. Italian Journal of Prevention, Diagnostic and Therapeutic Medicine. 6, 2 (giu. 2023), 40-43. DOI:https://doi.org/10.30459/2023-9.

Riferimenti bibliografici

- Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. https://doi.org/10.1080/1364557032000119616.

- Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity.

- Davenport, T., &Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/fhj.2019-0021.

- Elo, S., &Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x.

- Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review. https://doi.org/10.1162/99608f92.8cd550d1.

- Frizzell, J. D., Liang, L., Schulte, P. J., Yancy, C. W., Heidenreich, P. A., Hernandez, A. F., Bhatt, D. L., Fonarow, G. C., &Laskey, W. K. (2021). Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches. JAMA Cardiology, 2(2), 204-209.

- Gordon, W. J., &Landman, A. (2022). Artificial Intelligence in Health Care: Anticipating Challenges to Ethics, Privacy, and Bias. Perspectives in Biology and Medicine, 65(1), 45-57.

- Kwon, J. M., Lee, Y., Lee, Y., & Lee, S. (2022). An Algorithm Using 12-Lead Electrocardiography to Predict Paroxysmal Atrial Fibrillation. Journal of the American Heart Association, 7(2), e007093.

- Holmes, O., Ayers, S., Duarte, C., &Falzon, L. (2021). AI in mental health: Exploring the attitudes of practitioners towards AI in psychological therapy. Counselling and Psychotherapy Research, 21(4), 752-762.

- Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5(1), 69. https://doi.org/10.1186/1748-5908-5-69.

- Martinez-Martin, N., Kreitmair, K., & Char, D. (2020). Ethical Issues for Direct-to-Consumer Digital Psychotherapy Apps: Addressing Accountability, Data Protection, and Consent. JMIR Mental Health, 5(2), e32.

- Munn, Z., Peters, M. D., Stern, C., Tufanaru, C., McArthur, A., &Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18(1), 143. https://doi.org/10.1186/s12874-018-0611-x.

- Peters, M. D., Godfrey, C. M., Khalil, H., McInerney, P., Parker, D., &Soares, C. B. (2015). Guidance for conducting systematic scoping reviews. International Journal of Evidence-Based Healthcare, 13(3), 141–146. https://doi.org/10.1097/XEB.0000000000000050.

- Reddy, S. (2021). The Impact of Artificial Intelligence - Widespread Job Losses. IEEE Spectrum: Technology, Engineering, and Science News. https://spectrum.ieee.org/job-losses-from-ai-there-are-some-things-we-can-do.

- Rudin, C., & Chen, Y. (2020). AI in Health Care: Anticipating Challenges and Opportunities. Journal of the American Medical Association, 324(18), 1837–1838.

- Saria, S., Rajani, A. K., Gould, J., Koller, D., & Penn, A. A. (2020). Integration of early physiological responses predicts later illness severity in preterm infants. Science Translational Medicine, 2(48), 48ra65-48ra65. https://doi.org/10.1126/scitranslmed.3001304.

- Taylor, R. A., Pare, J. R., Venkatesh, A. K., Mowafi, H., Melnick, E. R., Fleischman, W., & Hall, M. K. (2020). Prediction of In-hospital Mortality in Emergency Department Patients with Sepsis: A Local Big Data–Driven, Machine Learning Approach. Academic Emergency Medicine, 23(3), 269–278.

- Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7.

- Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

- Wong, Z. Y., Zhou, J., & Zhang, Q. (2021). Artificial intelligence for infectious disease Big Data Analytics. Infection, Genetics and Evolution, 77, 104061.

- Wu, X., Guo, X., Zhang, Z. (2021). The Efficacy of Mobile Health Apps for Self-Management in Patients with Chronic Illness: A Systematic Review and Meta-Analysis. Journal of Telemedicine and Telecare, 27(5), 261-271.

Puoi leggere altri articoli dello stesso autore/i