Google's development of Med-PaLM and its successor, Med-PaLM 2, signifies a major leap forward in the application of artificial intelligence (AI) in healthcare, particularly in medical question-answering and the processing of diverse biomedical data. Med-PaLM 2, a large language model (LLM), significantly improved upon its predecessor by scoring 85% on US Medical License Exam questions, showcasing its ability to handle complex multimodal data such as clinical language, medical imaging, and genomics. Built upon pretrained models like the Pathways Language Model (PaLM) and Vision Transformer (ViT), Med-PaLM integrates these capabilities to outperform state-of-the-art specialist models in various tasks within the MultiMedBench benchmark. Despite these achievements, Med-PaLM faces challenges related to fairness, ethical considerations, and potential biases in its outputs, necessitating ongoing research and collaboration among AI researchers, medical professionals, and ethicists to ensure its responsible and equitable deployment. Google's initiative also includes the creation of benchmarks like MultiMedBench and MultiMedQA to foster transparency and collaboration in AI research, although these benchmarks have limitations in dataset size and modality diversity.