Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging - Wellnhofer, Ernst (Frontiers in Cardiovascular Medicine, 2022)
Abstract Recent progress in digital health data recording, advances in computing power, and methodological approaches that extract information from data as artificial intelligence are expected to have a disruptive impact on technology in medicine. One of the potential benefits is the ability to extract new and important insights from the vast amount of data generated during the delivery of health care every day. Cardiovascular imaging is boosted by new intelligent automatic methods to manage, process, segment, and analyze petabytes of image data exceeding historical manual capacities. Algorithms that learn from data raise new challenges for regulatory bodies. Partially autonomous behavior and adaptive modifications as well as lack of transparency in deriving evidence from complex data pose considerable problems. Controlling new technologies requires new controlling techniques and ongoing regulatory research. All stakeholders must take part in the quest of finding a fair balance between innovation and regulation. The regulatory approach to artificial intelligence must be risk-based and resilient. A focus on unknown emerging risks demands continuous surveillance and clinical evaluation during the total product life cycle. Since learning algorithms are data-driven, high-quality data is fundamental for good machine learning practice. Mining, processing, validation, governance, and control of data must account for bias, error, inappropriate use, drifts, and shifts, particularly of real-world data. Regulators all over the world are currently tackling the 21st-century challenges raised by “learning” medical devices. We present ethical concerns and different regulatory approaches. Finally, we embark on a discussion on the future of responsible artificial intelligence.