Ethics in AI for Radiology and Oncology: Multi-Method Study Findings

A 132.6 KB PDF document presenting findings from a multi-method empirical study on ethical and societal aspects of AI in radiology and oncology. The work was conducted within the EuCanImage project by author Mónica Cano Abadía and last updated on April 20, 2026. It is licensed under CC-BY-4.0 and available on figshare.

Use Cases

• Informing ethical AI development frameworks based on the identified dimensions of trustworthy AI.
• Designing stakeholder engagement strategies for AI in cancer care based on the relational model of trustworthiness.
• Analyzing the operationalization gap between abstract principles and clinical practice in radiological AI.
• Studying the socio-technical dynamics shaping ethical issues like bias and accountability in real-world medical AI settings.

Strengths

• Provides a structured, multi-stakeholder development pathway derived from empirical research.
• Identifies four concrete guiding dimensions for trustworthy AI: explainability, trust, accountability, and justice.
• Based on a multi-method study involving literature reviews, interviews, and workshops with diverse stakeholders.

Limitations

• The dataset is a 132.6 KB PDF document, indicating a limited scope focused on study findings rather than primary data.
• Row count and column-level documentation are absent; the content is textual analysis.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Multi-method empirical study involving literature reviews, interviews, and workshops.

Time Range

Study conducted within the EuCanImage project; specific temporal coverage not provided.

Freshness

Last updated 2026-04-20 05:19:36; freshness should be verified.

Geography

Geographic coverage is not specified.