The field of medical image segmentation and analysis is moving towards more streamlined and effective approaches, leveraging advances in multimodal learning and large language models. Researchers are exploring novel frameworks that reformulate traditional tasks, such as segmenting target regions in medical images based on natural language descriptions, as autoregressive next-token prediction tasks. This allows for more unified architectures and the use of pretrained tokenizers, enhancing generalization and adaptability. Additionally, there is a growing interest in developing methods that promote bidirectional interaction between vision and language modalities, enabling more effective modeling and interpretability. Noteworthy papers include: NTP-MRISeg, which achieves state-of-the-art performance on medical referring image segmentation tasks by reformulating the task as an autoregressive next-token prediction task. Libra-MIL, which introduces a novel approach to multimodal prototype-based multi-instance learning, promoting bidirectional interaction and generalizable feature learning. ProSona, which enables controllable personalization of medical image segmentation via natural language prompts, reducing inter-observer variability and improving accuracy. vMFCoOp, which proposes a framework for aligning semantic biases between large language models and vision-language models, achieving robust biomedical prompting and superior few-shot classification.