Medical artificial intelligence holds immense promise, offering the potential to analyze massive datasets, detect intricate patterns, and deliver consistent, tireless support to healthcare professionals. Yet despite the proliferation of AI models developed in academic and industrial labs—thousands of which have been created in recent years—very few have made a meaningful impact in actual clinical practice. One of the primary reasons for this gap lies in the lack of contextual understanding within current models. Most AI systems are trained on isolated datasets—often de-identified patient records, imaging scans, or lab results—without sufficient integration of the broader clinical context. This includes patient history, real-time vital signs, treatment goals, socioeconomic factors, and the nuances of physician-patient communication. Without this context, models may produce accurate predictions in controlled environments but fail when applied in complex, dynamic clinical settings. Another challenge is the misalignment between model performance metrics and clinical needs. Many AI tools are evaluated using standard benchmarks like accuracy or F1 scores, which don’t reflect how well a model supports decision-making in real-world workflows. A model might perform well statistically but offer recommendations that are impractical, unclear, or difficult to integrate into existing electronic health record systems. Interoperability is also a persistent issue. Clinical data is often siloed across different departments, hospitals, and software platforms. AI models struggle to access or interpret data consistently when it’s stored in incompatible formats or lacks standardization. To bridge this gap, researchers and developers must prioritize context-aware design from the outset. This means training models not just on data, but on data within its clinical narrative—incorporating temporal sequences, treatment trajectories, and patient-specific variables. Federated learning, which allows models to learn from distributed data without centralizing sensitive information, offers a promising path forward while preserving privacy. Additionally, involving clinicians throughout the development process is critical. Co-design approaches—where doctors, nurses, and other healthcare providers collaborate with engineers and data scientists—can ensure that AI tools align with real-world workflows, address actual pain points, and support, rather than disrupt, clinical decision-making. Explainability and transparency are also essential. Clinicians need to understand how an AI arrives at a recommendation, especially in high-stakes scenarios. Tools that provide interpretable outputs, such as visualizations of key contributing factors or confidence intervals, can build trust and encourage adoption. Finally, regulatory and ethical frameworks must evolve to support responsible deployment. Regulatory bodies should require not just technical validation, but evidence of clinical utility and integration feasibility before approving AI tools for use. Ultimately, the success of medical AI depends not on the sophistication of the algorithm alone, but on how well it fits into the complex, human-centered reality of healthcare. By embedding context, collaboration, and clinical relevance into the core of AI development, we can move beyond promising prototypes and deliver tools that truly improve patient care.