The field of medical data analysis is experiencing a significant shift towards more transparent, adaptable, and clinically aligned AI systems. Recent developments have focused on creating agentic AI frameworks that can automate the entire clinical data pipeline, from ingestion to inference, and provide interactive decision support. These frameworks integrate multiple components, including data preprocessing, feature extraction, model selection, and interpretation, to enable seamless and cost-effective analysis of medical data. Notably, the use of Large Language Models (LLMs) and visual linguistic explainability agents is becoming increasingly prominent, allowing for more comprehensive and contextualized analysis of medical images. Furthermore, there is a growing emphasis on ensuring the reproducibility and interpretability of machine learning workflows, with a focus on transparent experimental data analytics provenance. Overall, these advancements are transforming the field of medical data analysis, enabling clinicians and researchers to derive meaningful and actionable insights from complex data. Noteworthy papers include: AURA, which introduces a multi-modal medical agent for understanding, reasoning, and annotation of medical images, and Helix 1.0, which provides an open-source framework for reproducible and interpretable machine learning on tabular scientific data. Additionally, RDMA presents a cost-effective agent-driven approach for rare disease discovery within electronic health record systems, and the Agentic AI framework for End-to-End Medical Data Inference automates the entire clinical data pipeline, enabling scalable and cost-efficient analysis of medical data.