Ethical AI in Radiology and Oncology: Multi-Method Study Findings

EuCanImage project findings on embedding ethics for trustworthy AI in radiology and oncology. The 147.0 KB PDF document presents results from a multi-method empirical study involving literature reviews, interviews, and workshops with developers and clinicians. Authored by Mónica Cano Abadía and last updated in April 2026, it explores the operationalization of ethical principles like explainability and fairness in clinical AI.

Use Cases

• Designing stakeholder engagement frameworks for AI development based on the described multi-method study approach.
• Analyzing barriers to operationalizing ethical principles like explainability and fairness in clinical AI systems.
• Studying the socio-technical dynamics shaping ethical concerns in radiological AI based on interview and workshop findings.
• Developing procedural guidance for trustworthy AI in cancer care based on the proposed structured development pathway.

Strengths

• Provides concrete findings from a multi-method empirical study involving literature reviews, interviews, and workshops.
• Explicitly licensed under CC-BY-4.0 for open sharing and reuse.
• Authored by a named researcher, Mónica Cano Abadía, providing clear attribution.

Limitations

• Dataset is a 147.0 KB PDF document, which is a very small textual analysis rather than a large-scale data collection.
• Row count and column-level documentation are absent; the content is a research paper, not structured data.
• The data is a single research output; its findings may not be generalizable without further validation.

Provenance

Source

figshare

Collection Method

Multi-method empirical study involving literature reviews, interviews, and workshops within the EuCanImage project.

Time Range

Study period not specified; document last updated in April 2026.

Freshness

Last updated 2026-04-20 05:19:35

Geography

Geographic scope of the study not specified.