The field of medical AI is moving towards developing more reliable and accurate models for various applications, including disease diagnosis, cell counting, and medical language understanding. Researchers are focusing on improving the stability and generalizability of large language models, as well as enhancing the interpretability of vision-language models. Notable papers in this area include: Stabilizing Reasoning in Medical LLMs with Continued Pretraining and Reasoning Preference Optimization, which introduces a novel approach to optimizing medical language models for reliable explanations. Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation, which proposes a multi-resolution paradigm for pathology-language pre-training. AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries, which presents an innovative approach to cervical cancer screening using low-cost biological microscopes and efficient AI algorithms. Towards Improved Cervical Cancer Screening: Vision Transformer-Based Classification and Interpretability, which achieves high accuracy in cervical cell image classification using a vision transformer-based approach. A Method for the Architecture of a Medical Vertical Large Language Model Based on Deepseek R1, which proposes an efficient lightweight medical vertical large language model architecture method. Investigating Zero-Shot Diagnostic Pathology in Vision-Language Models with Efficient Prompt Design, which presents a systematic investigation of prompt engineering in computational pathology.