Since its inception in 2017, npj Digital Medicine has attracted a disproportionate number of manuscripts reporting on uses of artificial intelligence. This field has matured rapidly in the past several years. There was initial fascination with the algorithms themselves (machine learning, deep learning, convoluted neural networks) and the use of these algorithms to make predictions that often surpassed prevailing benchmarks. As the discipline has matured, individuals have called attention to aberrancies in the output of these algorithms. In particular, criticisms have been widely circulated that algorithmically developed models may have limited generalizability due to overfitting to the training data and may systematically perpetuate various forms of biases inherent in the training data, including race, gender, age, and health state or fitness level (Challen et al. BMJ Qual. Saf. 28:231–237, 2019; O’neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Broadway Book, 2016). Given our interest in publishing the highest quality papers and the growing volume of submissions using AI algorithms, we offer a list of criteria that authors should consider before submitting papers to npj Digital Medicine.

自2017年成立以来，npj Digital Medicine吸引了不成比例的手稿报道了人工智能的使用。在过去的几年中，这个领域已经迅速成熟。人们对算法本身（机器学习，深度学习，卷积神经网络）产生了最初的兴趣，并开始使用这些算法进行预测，而这些预测通常会超过主流基准。随着学科的成熟，人们呼吁在这些算法的输出中注意畸变。特别是，批评广泛流传，算法开发的模型由于对训练数据的过度拟合而可能具有局限性，并且可能系统地使训练数据固有的各种形式的偏见永存，包括种族，性别，年龄，健康状况或健康水平（Challen等人，BMJ Qual。Saf。28：231–237，2019; 奥尼尔 数学破坏武器：大数据如何增加不平等并威胁民主，百老汇书，2016年。鉴于我们对发表最高质量论文的兴趣以及使用AI算法提交的论文数量不断增加，我们提供了作者提交论文至npj数字医学。