The big data revolution, accompanied by the development and deployment of wearable medical devices and mobile health applications, has enabled the biomedical community to apply artificial intelligence (AI) and machine learning algorithms to vast amounts of data. This shift has created new research opportunities in predictive analytics, precision medicine, virtual diagnosis, patient monitoring, and drug discovery and delivery, which has garnered the interests of government, academic, and industry researchers alike and is already putting new tools in the hands of practitioners.
This boom in digital health opportunities has also raised numerous questions concerning the future of biomedical research and healthcare practices. How reliable are deployed AI-driven diagnostic tools, and what is the impact of these tools on doctors and patients? How vulnerable are algorithms to bias and unfairness? How can research improve the process of detecting unfairness in machine learning algorithms? How are other fields simultaneously advancing AI applications? How will academia prepare scientists with the skills to meet the demands of the newly transformed industry? Informed answers to these and other questions require interdisciplinary discussion and collaboration. On February 13 and 14, 2018, the National Academies of Sciences, Engineering, and Medicine convened a workshop to explore these and other questions related to the emerging use of AI and machine learning technologies in translational research. This publication summarizes the presentations and discussions from the workshop.
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|Artificial Intelligence and Machine Learning to Accelerate Translational Research: Proceedings of a Workshop - in Brief||1-9|
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