Frontiers in digital health · 2026
Oncologists' knowledge, attitudes and needs about artificial intelligence in clinical oncology in Luxembourg in 2026: a national cross-sectional survey (AICO study).
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Kaddu-Mulindwa Dominic, Mao Xianqing, Duhem Caroline, de Wilde Sigrid, Rauh Stefan, Klein Gilles, Jacob Frank, Servais Sophie
Аннотация
INTRODUCTION: Artificial intelligence (AI) is rapidly entering the field of medical oncology. Clinician literacy, ethical awareness, and governance structures are prerequisites for safe implementation, yet real-world data on adoption, training and ethical perceptions from small European healthcare systems remain scarce.
METHODS: We conducted an anonymous national cross-sectional online survey among all practicing physicians (medical oncologists, radiation oncologists, haematologist-oncologists) and oncology residents in Luxembourg (n = 42 eligible) between January and February 2026. The instrument covered AI knowledge, use, attitudes, ethical/legal perspectives, barriers and training needs. Proportions are reported with 95% Wilson score confidence intervals; analyses were descriptive.
RESULTS: In total 25 physicians responded (59.5%), 88% (95% CI 70.0-95.8) of whom had no formal AI training. All respondents had used large language models (LLMs), with 52% (33.5-70.0) reporting professional use for non-clinical tasks and 20% (8.9-39.1) for clinical decision support. Most participants (84%; 65.3-93.6) supported the use of AI as a clinical decision-support tool, yet 88% (70.0-95.8) of physicians perceived they would bear primarily legal responsibility for AI-related errors. The main barriers to implementation were lack of validated tools (68%; 48.4-82.8) and regulatory/legal uncertainty (64%; 44.5-79.8). Notably, 76% (56.6-88.5) of respondents reported encountering patients who brought AI-generated medical information to consultations.
DISCUSSION: AI adoption among oncologists in Luxembourg is already widespread, including emerging clinical use, despite limited formal training and unresolved medico-legal frameworks. This "implementation-governance gap" highlights the need for structured education, validated clinical tools, and regulatory clarity to ensure safe and ethically sound integration of AI into oncology practice.
METHODS: We conducted an anonymous national cross-sectional online survey among all practicing physicians (medical oncologists, radiation oncologists, haematologist-oncologists) and oncology residents in Luxembourg (n = 42 eligible) between January and February 2026. The instrument covered AI knowledge, use, attitudes, ethical/legal perspectives, barriers and training needs. Proportions are reported with 95% Wilson score confidence intervals; analyses were descriptive.
RESULTS: In total 25 physicians responded (59.5%), 88% (95% CI 70.0-95.8) of whom had no formal AI training. All respondents had used large language models (LLMs), with 52% (33.5-70.0) reporting professional use for non-clinical tasks and 20% (8.9-39.1) for clinical decision support. Most participants (84%; 65.3-93.6) supported the use of AI as a clinical decision-support tool, yet 88% (70.0-95.8) of physicians perceived they would bear primarily legal responsibility for AI-related errors. The main barriers to implementation were lack of validated tools (68%; 48.4-82.8) and regulatory/legal uncertainty (64%; 44.5-79.8). Notably, 76% (56.6-88.5) of respondents reported encountering patients who brought AI-generated medical information to consultations.
DISCUSSION: AI adoption among oncologists in Luxembourg is already widespread, including emerging clinical use, despite limited formal training and unresolved medico-legal frameworks. This "implementation-governance gap" highlights the need for structured education, validated clinical tools, and regulatory clarity to ensure safe and ethically sound integration of AI into oncology practice.