The adoption of artificial intelligence (AI) in healthcare is accelerating, yet successful implementations of physician-facing AI systems remain limited and uneven. This paper presents a literature review of 40 peer-reviewed studies published between November 2022 and November 2024, spanning clinical, technical, and human-computer interaction (HCI) domains. Anchored in a socio-technical perspective, the review examines our existing understanding of how technical design, user expertise, and organizational factors shape the effectiveness of AI systems in real-world clinical settings. Our analysis identifies two meta-themes: (1) context as a dynamic, multi-level influence that actively reshapes AI system behavior, and (2) trust as an emergent property that evolves over time through clinician experience, team dynamics, and institutional feedback. These insights challenge static models of implementation and highlight the need for adaptive governance mechanisms that support continuous monitoring, runtime oversight, and trust calibration. We propose actionable recommendations for healthcare leaders, implementation teams, and assurance functions, and propose future research directions grounded in control theory, organizational learning, and complex adaptive systems theory. By integrating perspectives from HCI and clinical informatics, this review provides a foundation for designing AI systems that not only are technically robust, trustworthy, and sustainable but also have contextual awareness capabilities in high-stakes healthcare environments.