The field of medical imaging and diagnostic AI is rapidly advancing, with a focus on developing more accurate and reliable models for disease diagnosis and treatment. Recent research has emphasized the importance of multimodal approaches, which integrate multiple data sources such as images, text, and audio to improve diagnostic performance. One of the key challenges in this area is addressing the issue of hallucinations in medical visual question answering models, where models generate answers that are not supported by the input image. To address this, researchers have proposed novel evaluation protocols and benchmarks, such as HEAL-MedVQA, to assess the localization abilities and hallucination robustness of medical large multimodal models. Another significant area of research is the development of mixed-reality visualization platforms, such as PathVis, which aim to enhance the diagnostic workflow by providing immersive and interactive visualization of medical images. Furthermore, there is a growing interest in using causal inference frameworks to eliminate cross-modal bias in medical visual question answering tasks, and in developing anatomical ontology-guided reasoning frameworks to enhance the interactivity and explainability of medical large multimodal models. Notable papers in this area include Localizing Before Answering: A Benchmark for Grounded Medical Visual Question Answering, CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning, and Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer. These papers demonstrate significant advancements in the field, with potential applications in clinical decision support, disease diagnosis, and medical image analysis.