An Outside of the healthcare industry, commercially available artificial intelligence (AI) algorithms have shown signs of racial, gender, and societal prejudice. The development of AI algorithms in the fields of radiologic sciences and healthcare is significantly impacted by these biases. The physician community should work with developers and regulators to create paths that guarantee algorithms sold for widespread clinical practice are secure, efficient, and free of unintended bias in order to prevent the introduction of bias in healthcare AI. Structured AI use cases with data elements have been developed by the ACR Data Science Institute to make it easier to create standardized datasets for AI testing and training at various universities. This project seeks to encourage the accessibility of a variety of data for algorithm development. Additionally, ACR Certify-AI and ACR Assess-AI, validation and monitoring services offered by the ACR Data Science Institute, integrate guidelines for minimizing algorithm bias and advancing health fairness. The ACR should support pricing methods for AI that guarantee access to AI technologies for all patients, regardless of their socioeconomic l
📖 افتح في inklap 🔗 DOI 📮 اطلب بحثاً