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Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze visual and textual medical data. Our paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on publicly available models designed for medical report generation and visual question answering (VQA). We provide background on NLP and CV, explaining how techniques from both fields are integrated into VLMs, with visual and language data often fused using Transformer-based architectures to enable effective learning from multimodal data. Key areas we address include the exploration of 18 public medical vision-language datasets, in-depth analyses of the architectures and pre-training strategies of 16 recent noteworthy medical VLMs, and comprehensive discussion on evaluation metrics for assessing VLMs' performance in medical report generation and VQA. We also highlight current challenges facing medical VLM development, including limited data availability, concerns with data privacy, and lack of proper evaluation metrics, among others, while also proposing future directions to address these obstacles. Overall, our review summarizes the recent progress in developing VLMs to harness multimodal medical data for improved healthcare applications. 

We apply topological data analysis to the behavior of C. elegans , a widely studied model organism in biology. In particular, we use topology to produce a quantitative summary of complex behavior which may be applied to high-throughput data. Our methods allow us to distinguish and classify videos from various environmental conditions and we analyze the trade-off between accuracy and interpretability. Furthermore, we present a novel technique for visualizing the outputs of our analysis in terms of the input. Specifically, we use representative cycles of persistent homology to produce synthetic videos of stereotypical behaviors.