Differential Diagnosis (DDx) is the process of identifying the most likely
medical condition among the possible pathologies through the process of
elimination based on evidence. An automated process that narrows a large set of
pathologies down to the most likely pathologies will be of great importance.
The primary prior works have relied on the Reinforcement Learning (RL) paradigm
under the intuition that it aligns better with how physicians perform DDx. In
this paper, we show that a generative approach trained with simpler supervised
and self-supervised learning signals can achieve superior results on the
current benchmark. The proposed Transformer-based generative network, named
DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and
predicts the actual pathology using a neural network. Experiments are performed
using the DDXPlus dataset. In the case of DDx, the proposed network has
achieved a mean accuracy of 99.82% and a mean F1 score of 0.9472. Additionally,
mean accuracy reaches 99.98% with a mean F1 score of 0.9949 while predicting
ground truth pathology. The proposed DDxT outperformed the previous RL-based
approaches by a big margin. Overall, the automated Transformer-based DDx
generative model has the potential to become a useful tool for a physician in
times of urgency.

通过使用基于 Transformer 的生成模型 DDxT，该论文展示了一种自动化的差异诊断方法，其在消除了大量可能的病理学情况后能够生成可能的病理学选择，并使用神经网络预测真实的病理学，相较于以往基于强化学习的方法在当前基准测试中取得了更优秀的结果。总体而言，这种自动化的 Transformer-based DDx 生成模型有潜在成为紧急情况下医生的有用工具的可能性。