Author: Dayana

Artificial intelligence in radiology depends on diverse data sources to train algorithms, enhance diagnostic accuracy, and assist radiologists in medical image interpretation.

Artificial Intelligence (AI) in radiology leverages machine learning and advanced technologies to enhance the interpretation of medical images, aiding radiologists in improving healthcare outcomes.

Ensuring the reproducibility and validation of scientific results is essential, and RSNA has developed checklists and standards for this purpose, targeting audiences such as researchers, medical professionals, AI developers, students, and policymakers.

The ECLAIR guidelines provide a structured approach for evaluating commercial AI solutions in radiology, aiding stakeholders in assessing various factors like relevance, performance, usability, integration, compliance, financial viability, and support services.

Regulatory bodies globally oversee the approval and usage of AI technologies in radiology to ensure safety and efficacy. Each region has its distinct regulatory frameworks and certifications. In the US, the FDA handles approvals; the EU uses CE Marking via EMA; Health Canada grants Medical Device Licenses

Artificial intelligence (AI) is playing an increasingly vital role in radiology, driven by the development in machine learning and computational power. While it's not imperative for radiologists to have deep AI expertise, basic knowledge can significantly enhance their practice.