Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Attitudes of older patients toward artificial intelligence in decision-making in healthcare
4
Zitationen
12
Autoren
2025
Jahr
Abstract
Artificial intelligence (AI) is rapidly transforming healthcare, promising improved diagnostics and efficiency. However, the ethical implications and patient perspectives, particularly among older adults, remain underexplored. This study aimed to explore the ethical considerations and attitudes of older patients toward AI in healthcare decision-making. We conducted qualitative research in the geriatric wards of three hospitals in Dhaka, Bangladesh, using the Technology Acceptance Model and a phenomenological approach as the guiding frameworks. Semi-structured interviews were conducted with 21 purposively sampled participants, all aged 60 and above. Data were collected in Bangla, transcribed, translated into English, and analyzed through thematic analysis. Methodological rigor was maintained through member checking, triangulation, and reflexive practices. Five key themes emerged: (1) Trust and skepticism toward AI’s decision-making capabilities, with concerns about its ability to address nuanced health needs; (2) A strong preference for human interaction over technological efficiency, highlighting the irreplaceable value of empathy; (3) Ethical concerns regarding informed consent, emphasizing the need for transparent and comprehensible AI integration; (4) Apprehension about privacy and data security, reflecting a trust deficit in AI’s handling of sensitive health information; and (5) Mixed perceptions on AI’s role in enhancing or diminishing the quality of care. This study underscores the need for transparent, patient-centered AI systems that enhance, rather than replace, human elements in care. Addressing ethical concerns about privacy, autonomy, and informed consent is critical to fostering trust and acceptance among older patients.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.100 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.466 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 · 5.429 Zit.
Autoren
Institutionen
- International University of Business Agriculture and Technology(BD)
- Bangladesh Medical University(BD)
- Rajshahi Medical College(BD)
- Daffodil International University(BD)
- National Institute of Nuclear Medicine & Allied Sciences(BD)
- Leading University(BD)
- Hiroshima University(JP)
- Shahjalal University of Science and Technology(BD)
- Islamic University(BD)
- Pundra University of Science and Technology(BD)
- Armed Forces Medical College(BD)
- Western Norway University of Applied Sciences(NO)