  Methodological advancements to automate the generation of differential
diagnosis (DDx) to predict a list of potential diseases as differentials given
patients' symptom descriptions are critical to clinical reasoning and
applications such as decision support. However, providing reasoning or
interpretation for these differential diagnoses is more meaningful.
Fortunately, large language models (LLMs) possess powerful language processing
abilities and have been proven effective in various related tasks. Motivated by
this potential, we investigate the use of LLMs for interpretable DDx. First, we
develop a new DDx dataset with expert-derived interpretation on 570 public
clinical notes. Second, we propose a novel framework, named Dual-Inf, that
enables LLMs to conduct bidirectional inference for interpretation. Both human
and automated evaluation demonstrate the effectiveness of Dual-Inf in
predicting differentials and diagnosis explanations. Specifically, the
performance improvement of Dual-Inf over the baseline methods exceeds 32%
w.r.t. BERTScore in DDx interpretation. Furthermore, experiments verify that
Dual-Inf (1) makes fewer errors in interpretation, (2) has great
generalizability, (3) is promising for rare disease diagnosis and explanation.
