Artificial intelligence is poised to revolutionize the field of medicine, however significant questions must be answered prior to its implementation on a regular basis. Many artificial intelligence algorithms remain limited by isolated datasets which may cause selection bias and truncated learning for the program. While a central database may solve this issue, several barriers such as security, patient consent, and management structure prevent this from being implemented. An additional barrier to daily use is device approval by the Food and Drug Administration. In order for this to occur, clinical studies must address new endpoints, including and beyond the traditional bio- and medical statistics. These must showcase artificial intelligence’s benefit and answer key questions, including challenges posed in the field of medical ethics.

人工智能有望彻底改变医学领域，但必须在定期实施之前回答重大问题。许多人工智能算法仍然受到孤立数据集的限制，这可能会导致程序的选择偏差和截断学习。虽然中央数据库可以解决这个问题，但安全性、患者同意和管理结构等几个障碍阻止了这一点的实施。日常使用的另一个障碍是食品和药物管理局的设备批准。为了实现这一点，临床研究必须解决新的终点，包括并超越传统的生物和医学统计。这些必须展示人工智能的好处并回答关键问题，包括医学伦理领域提出的挑战。