The rapid adoption of large language models (LLMs) in healthcare has indicated both transformative opportunism and mounting complexities. Moving beyond automation, Human-AI Collaboration (HAIC) requires careful and considered attention to transparency, trust calibration, collaborative control, and ethical culpability. At present, most assessment procedures evaluate the performance of human and machine understudies separately, instead of evaluating human–machine (collaborative) outcomes. This chapter offered a collective horizon of three anchor points (Collaboration, Adaptation, and Regulation). Collaboration regards shared autonomy, trust, and workflow context, and is both desired and expected to function as a teammate rather than just as an agent. Adaptive explanations are critical to establishing psychological safety and usability and require the system to develop and revamp explanations on-the-fly in ways that use the user profile, situational cues, and task aspects of the situation. Regulatory readiness (especially, under applicable frameworks e.g., EU AI Act, GDPR, and Canada’s AIDA etc.) are needed to create transparency, oversight, and direct accountability. These are contributing elements to develop a basis for integrating LLMs in the healthcare sector, which is ethical, responsible, safe, and human-centered, and meaningfully positioned for an organization, not only towards technical performance but also toward ethical, legal, and social entertainments.