The field of healthcare is witnessing a significant shift towards AI-driven innovations, with a focus on leveraging large language models (LLMs) to improve clinical decision-making, patient care, and resource allocation. Recent developments have highlighted the potential of LLMs in clinical information extraction, medical triage, and patient-centric e-health systems. However, concerns regarding data privacy, transparency, and the risk of hallucinations remain a challenge. Researchers are exploring novel approaches, such as multi-agent systems and cascading language model chains, to address these limitations and improve the accuracy and reliability of AI-driven healthcare solutions. Noteworthy papers in this area include: Leveraging Open-Source Large Language Models for Clinical Information Extraction in Resource-Constrained Settings, which demonstrated the effectiveness of open-source LLMs in clinical information extraction. Toward the Autonomous AI Doctor: Quantitative Benchmarking of an Autonomous Agentic AI Versus Board-Certified Clinicians in a Real World Setting, which showed strong diagnostic and treatment plan concordance between an autonomous AI system and human clinicians.