Algorithmic empathy in nursing – can artificial intelligence support humanism in care
DOI:
https://doi.org/10.12923/pielxxiw-2026-0039Keywords:
therapeutic relationship, algorithmic empathy, socially assistive robots, artificial intelligence in nursingAbstract
Introduction. Artificial intelligence increasingly supports communication in healthcare by offering tools that simulate empathy and reduce patients’ sense of isolation, particularly in telehealth and senior care.
Aim. The aim of this study was to analyze the mechanisms, opportunities and limitations of algorithmic empathy and digital empathy in healthcare, with particular emphasis on nursing practice.
Methods. A scoping review was conducted in accordance with the PRISMA-ScR (2020) guidelines. Literature searches were performed in the PubMed/MEDLINE, Scopus, Web of Science, CINAHL and Google Scholar, covering publications from 2015 to 2025. Empirical studies, literature reviews and conceptual analyses addressing algorithmic empathy, digital empathy, socially assistive robots, and emotion recognition systems in healthcare were included. A descriptive synthesis was applied.
Conclusions. Seven publications were included in the analysis. The results indicate that technological solutions may elicit responses perceived as empathic, which is associated with improved subjective well-being, reduced negative emotions and increased user engagement, primarily in long-term care, geriatric care and telehealth settings. Variability in intervention effectiveness and clinical context–related limitations were also noted. Algorithmic and digital empathy may support nursing care in conditions of limited direct contact. However, the use of such conditions requires professional oversight and a clear distinction between clinical empathy and technological solutions.
References
1. Liu Y, Wang K, Wei L, et al. Affective computing for healthcare: recent trends, applications, challenges, and beyond. Cornell University: Computer Sciences. 2024. https://doi.org/10.48550/arXiv.2402.13589
2. Schlicher M, Li Y, Murthy SMK, et al. Emotionally adaptive support: a narrative review of affective computing for mental health. Frontiers in Digital Health. Frontiers Media SA. 2025;7: 1-15. https://doi.org/10.3389/fdgth.2025.1657031
3. Seitz L. Artificial empathy in healthcare chatbots: Does it feel authentic? Comput. Hum. Behav. Artif. Hum. 2024;2(1):100067. https://doi.org/10.1016/j. chbah.2024.100067
4. Abou Hashish EA. Compassion through technology: Digital empathy concept analysis and implications in nursing. Digit. Health. 2025;11:1-14. https://doi. org/10.1177/205520762513262
5. Cora R. Integrating digital empathy in nursing education to improve patient outcomes in a digital world: A walker and avant conceptual analysis. Nursing Forum. 2025;4688531:1-6. https://doi.org/10.1155/nuf/4688531
6. Chen D, Chauhan K, Parsa R, et al. Patient perceptions of empathy in physician and artificial intelligence chatbot responses to patient questions about cancer. NPJ Digit. Med. 2025;8(1):275 https://doi.org/10.1038/s41746-025-01671-6
7. Jones NL, Loizides F, Jones K. Acceptance of needy socially assistive robots: A systematic review. Int. J. Soc. Robot.2025;17:1289-1303. https://doi.org/10.1007/ s12369-025-01272-z
8. El Arab RA, Alshakihs AH, Alabdulwahab SH, et al. Artificial intelligence in nursing: a systematic review of attitudes, literacy, readiness, and adoption intentions among nursing students and practicing nurses. Front. Digit. Health. 2025;7:1-17. https://doi. org/10.3389/fdgth.2025.1666005
9. Hung L, Zhao Y, Alfares H, et al. Ethical considerations in the use of social robots for supporting mental health and wellbeing in older adults in long-term care. Front. Robot AI. 2025;12:1-8. https://doi.org/10.3389/frobt.2025.1560214
10. Akingbola A, Adeleke O, Idris A, et al. Artificial intelligence and the dehumanization of patient care. J. Med. Surg. Public Health. 2024;3:100138. https://doi.org/10.1016/j. glmedi.2024.100138
11. Volpe U, Ramalho R, Gaebel W. Risks of digitalization in mental health care. neuropsychiatrie. Neuropsychiatria. 2025;7:37-40. https://doi.org/10.1007/s40211¬025-00551-5
12. Efendić E, Van de Calseyde PPFM, Evans AM. Slow response times undermine trust in algorithmic (but not human) predictions. Organ Behav. Hum. Decis. Process. 2020;157:103-114. https://doi.org/10.1016/j.obhdp.2020.01.008
13. Tricco AC, Lillie E, Zarin W, et al. PRISMA Extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann. Intern. Med. 2018;169(7):467-473. https://doi. org/10.7326/M18-0850
14. Simera I, Moher D, Hoey J, et al. A catalogue of reporting guidelines for health research. Eur. J. Clin. Invest. 2010;40(1):35-53. https://doi.org/10.1111/j.1365¬2362.2009.02234.x
15. de Gennaro M, Krumhuber EG, Lucas G. Effectiveness of an empathic chatbot in combating adverse effects of social exclusion on mood. Front. Psychol. 2020;10:1-14. https://doi.org/10.3389/fpsyg.2019.03061
16. Abdollahi H, Mahoor MH, Zandie R, et al. Artificial emotional intelligence in socially assistive robots for older adults: A pilot study. IEEE Trans. Affect. Comput. 2023;14(3):2020-2032. https://doi.org/10.48550/arXiv.2201.11167
17. Hung L, Liu C, Woldum E, et al. The benefits of and barriers to using a social robot PARO in care settings: A scoping review. BMC Geriatr. 2019;19:232. https://doi. org/10.1186/s12877-019-1244-6
18. Koban K, Banks J. It feels, therefore it is: Associations between mind perception and mind ascription for social robots. Comput. Human Behav. 2024;153:108098. https:// doi.org/10.1016/j.chb.2023.108098
19. Barker D, Tippireddy MKR, et al. Ethical considerations in emotion recognition research. Psychol. Int. 2025;7(2):43. https://doi.org/10.3390/psycholint7020043
20. Abujaber AA, Nashwan AJ. Ethical framework for artificial intelligence in healthcare research: A path to integrity. World J. Methodol. 2024;14(3):94071 https://dx.doi. org/10.5662/wjm.v14.i3.94071
21. Yıldız E. Artificial intelligence in mental health nursing: Balancing clinical efficiency and the human touch a quest for a new synthesis. J. Psychiatr. Ment. Health Nurs. 2025;32(4):946-952. https://doi.org/10.1111/jpm.131739
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Copyright (c) 2026 Małgorzata Kaszuba, Paweł Wiktorzak, Paweł Oskwarek, Marcin Kowalewski, Eryka Żygadło (Autor)

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