The field of medical language models is rapidly evolving, with a focus on improving the accuracy and reliability of models in generating medical reports, detecting errors, and providing explanations. Recent research has highlighted the importance of incorporating complex reasoning and reflection mechanisms into large vision-language models to enhance their performance in medical report generation. Additionally, there is a growing trend towards developing explainable language models that can provide transparent and trustworthy explanations for their predictions. The use of large language models has also shown promise in detecting errors in radiology reports, with some models achieving high accuracy in identifying errors. However, there is a need for more robust evaluation frameworks to assess the trustworthiness of natural language explanations generated by these models. Noteworthy papers include: LVMed-R2, which introduces a new fine-tuning strategy that incorporates complex reasoning and reflection mechanisms for medical report generation. On the Performance of an Explainable Language Model on PubMedQA, which presents an explainable language model that achieves state-of-the-art results on the PubmedQA dataset. Right Prediction, Wrong Reasoning: Uncovering LLM Misalignment in RA Disease Diagnosis, which highlights the misalignment between prediction accuracy and flawed reasoning in large language models for disease diagnosis.