The field of AI-driven healthcare and customer relationship management is rapidly evolving, with a focus on developing innovative solutions that leverage large language models (LLMs) and multi-agent systems. Recent developments have centered around improving the reliability and trustworthiness of medical vision-language models, enhancing the effectiveness of customer relationship management (CRM) systems, and facilitating more efficient human-AI collaboration. Notably, the integration of expert feedback and uncertainty estimation strategies has led to significant improvements in model performance and calibration. Furthermore, the application of reinforcement learning and prompt augmentation techniques has shown promise in enhancing the trustworthiness of multimodal large language models. Overall, these advancements have the potential to transform various aspects of healthcare and customer relationship management, enabling more accurate diagnoses, effective treatments, and personalized customer experiences. Noteworthy papers include: CRMAgent, which introduces a multi-agent system for generating high-quality CRM message templates, Uncertainty-Driven Expert Control, which proposes an expert-in-the-loop framework for aligning medical vision-language models with clinical expertise, Prompt4Trust, which presents a reinforcement learning framework for prompt augmentation targeting confidence calibration in multimodal large language models, MEDebiaser, which develops an interactive system for mitigating bias in multi-label medical image classification, Orchestrator-Agent Trust, which introduces a modular agentic AI visual classification framework with trust-aware orchestration, and Dr.Copilot, which supports Romanian-speaking doctors by evaluating and enhancing the presentation quality of their written responses.