Rianne Fijten
Biographic Data
| ID | 8657109 |
|---|---|
| NAME | Rianne Fijten |
| GIVEN NAMES | Rianne |
| FAMILY NAME | Fijten |
| SIGNATURE | FIJTEN R |
| AFFILIATIONS | Maastro Clinic |
| ORCID | 0000-0002-1964-6317 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Between map and maze
Artificial intelligence (AI) is often presented as a transformative technology for healthcare, promising to augment clinical decision-making, streamline workflows, and enhance diagnostic precision. Yet its integration into healthcare practice is shaped by the complex and often ambiguous notion of trust . While trust in AI has become a recurring theme across disciplines, there has been little systematic analysis of different conceptualizations of …
The Epistemic Cost of Opacity
Artificial intelligent (AI) systems used in medicine are often very reliable and accurate, but at the price of their being increasingly opaque. This raises the question whether a system’s opacity undermines the ability of medical doctors to acquire knowledge on the basis of its outputs. We investigate this question by focusing on a case in which a patient’s risk of recurring breast cancer is predicted by an opaque AI system. We argue that, given …
Clinician perspectives on clinical decision support systems in lung cancer
BACKGROUND: Lung cancer treatment decisions are typically made among clinical experts in a multidisciplinary tumour board (MTB) based on clinical data and guidelines. The rise of artificial intelligence and cultural shifts towards patient autonomy are changing the nature of clinical decision-making towards personalized treatments. This can be supported by clinical decision support systems (CDSSs) that generate personalized treatment information a…
No prominent works on this page.
Clinician perspectives on clinical decision support systems in lung cancer
BACKGROUND: Lung cancer treatment decisions are typically made among clinical experts in a multidisciplinary tumour board (MTB) based on clinical data and guidelines. The rise of artificial intelligence and cultural shifts towards patient autonomy are changing the nature of clinical decision-making towards personalized treatments. This can be supported by clinical decision support systems (CDSSs) that generate personalized treatment information a…
The Epistemic Cost of Opacity
Artificial intelligent (AI) systems used in medicine are often very reliable and accurate, but at the price of their being increasingly opaque. This raises the question whether a system’s opacity undermines the ability of medical doctors to acquire knowledge on the basis of its outputs. We investigate this question by focusing on a case in which a patient’s risk of recurring breast cancer is predicted by an opaque AI system. We argue that, given …
Between map and maze
Artificial intelligence (AI) is often presented as a transformative technology for healthcare, promising to augment clinical decision-making, streamline workflows, and enhance diagnostic precision. Yet its integration into healthcare practice is shaped by the complex and often ambiguous notion of trust . While trust in AI has become a recurring theme across disciplines, there has been little systematic analysis of different conceptualizations of …
Artificial Intelligence (2 works) · Artificial Intelligence in Healthcare and Education (2 works) · Clinical Reasoning and Diagnostic Skills (2 works) · Explainable Artificial Intelligence (XAI (2 works) · Medicine (2 works) · Alternative medicine (1 work) · Cancer survivorship and care (1 work) · Clinical decision support system (1 work) · Clinical Practice (1 work) · Cognitive reframing (1 work)