Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The AI Fever: Can Artificial Intelligence Replace Compassionate Human Care?
0
Zitationen
4
Autoren
2025
Jahr
Abstract
As healthcare professionals and educators, we have seen compassion as the backbone of quality of care.We vividly remember moments at patients' bedsides, where a gentle touch or a few calm words carried more weight than any medical treatment.These experiences remind us that healing is rooted in human connections as much as clinical skill.Compassion is a fundamental principle of healthcare, emphasized in ethical codes, care standards, and policy documents.It plays a unique role in delivering highquality treatment and serves as the foundation of human interactions in nursing. 1 Crawford et al define compassion as sensitivity to others' suffering, prompting verbal, non-verbal, or physical responses that help ease suffering. 2Zamanzadeh et al describe compassionate care as empathetic connection and active efforts to address patient concerns. 3olistic human connection, marked by attention to details and emotions and supportive care, is integral to positive healthcare experiences.The rise of advanced technologies such as artificial intelligence (AI) has increased concerns about whether human aspects of care might be replaced. 4Today, AI can analyze huge amounts of data in real time, assisting in disease identification, early diagnosis, and personalized treatment planning, and facilitating clinical decision-making. 5Advancements in AI have enhanced healthcare efficiency and accuracy.In nursing, AI can automate repetitive time-consuming tasks, such as recording patient data.As a result, it can help solve the problem of nursing shortages. 6Additionally, AI supports clinical practice by automating routine tasks and providing decision-support tools for healthcare
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.239 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.095 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.463 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.428 Zit.