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As artificial intelligence is increasingly integrated into clinical practice, various crucial challenges will persist, especially with regards to data acquisition, reporting, and potential re-identification of patient data. This paper outlines these challenges and suggests some open questions and potential solutions. Given recent news of companies overstepping their bounds in the pursuit of patient data to train their systems, and new regulations around privacy of those data, this discussion is especially pertinent. Here, we suggest that a common good can be achieved in which data can be kept private while also useful for artificial intelligence in the practice of medicine.