Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology
Abstract The integration of artificial intelligence (AI) in cardiovascular imaging has revolutionized the field, offering significant advancements in diagnostic accuracy and clinical efficiency. However, the complexity and opacity of AI models, particularly those involving machine learning (ML) and deep learning (DL), raise critical legal and ethical concerns due to their "black box" nature. This manuscript addresses these concerns by providing a comprehensive review of AI technologies in cardio
Record details
Published: 13 September 2024
Source: OpenAlex
Category: Research
Topics: Healthcare · Transparency
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
OpenAlex · 16 April 2024
Guiding AI in radiology: ESR’s recommendations for effective implementation of the European AI Act
OpenAlex · 13 February 2025
The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions
OpenAlex · 4 March 2025
Disclosing artificial intelligence use in scientific research and publication: When should disclosure be mandatory, optional, or unnecessary?
OpenAlex · 24 March 2025
Revised Surgical CAse REport (SCARE) Guideline: An Update for the Age of Artificial Intelligence
OpenAlex · 22 May 2025
Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges
OpenAlex · 29 August 2025
How to cite this record
ethics.ai (13 September 2024), “Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology,” evidence record 9653, https://ethics.ai/record/9653 (originally published by OpenAlex).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.