Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
AI-Driven Natural Language Processing in Healthcare: Transforming Patient-Provider Communication
56
Zitationen
2
Autoren
2024
Jahr
Abstract
Abstract: Healthcare communication is the lifeblood of effective patient care. The ability of patients and providers to exchange information, comprehends diagnoses, and collaboratively make decisions directly influences healthcare outcomes. In this context, AI-powered NLP has emerged as an invaluable agent of change, revolutionizing the way medical information is conveyed, understood, and acted upon. Through a comprehensive exploration, this review article unpacks the multifaceted facets of AI's role in healthcare communication. It begins by elucidating the essence of AI-powered NLP, providing readers with a foundational understanding of these transformative technologies. Subsequently, it delves into the myriad benefits that AI brings to the table, ranging from improved patient engagement and accessibility to streamlined clinical documentation and augmented diagnosis and treatment support. However, it's not all progress without pause. This review also delves into the ethical considerations intrinsic to AI in healthcare communication, such as safeguarding patient privacy and addressing bias and equity concerns. As the review work unfolds, it scrutinizes the challenges that must be surmounted to effectively implement AI-driven communication solutions in healthcare settings while casting a visionary gaze into the future, discerning the uncharted horizons where AI might further elevate healthcare communication. Keywords: AI, healthcare, Natural Language Processing, Patient Experience, Patient-Provider Communication, Quality of Care
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.231 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.084 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.444 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 · 5.423 Zit.