For the healthcare community, this study represents a major step forward in how healthcare systems can use AI powered data extraction to understand real world cancer outcomes. Instead of relying solely on databases or small, controlled studies, AI enables clinicians and researchers to analyze massive volumes of real patient data across hospitals, producing faster, more comprehensive insights into which treatments work best in practice. This approach could revolutionize clinical decision making, public health surveillance, and policy planning, allowing hospitals to track outcomes more accurately and tailor care based on regional or institutional differences. The study also reinforces the importance of combining AI efficiency with physician oversight because while AI can recognize patterns and summarize vast datasets, it cannot yet replace the nuanced judgment required for complex or rare cases. For the public and patient community, these findings show how AI can make medicine more personalized, accessible, and transparent. AI tools like those used in the study can turn complicated medical records into understandable information thus improving patient education and reducing confusion around treatment options. On a larger scale, using AI in cancer research could help reduce disparities by identifying gaps in care, monitoring early warning signs of disease progression, and optimizing treatment timing for better outcomes. Though challenges remain in refining AI accuracy and ensuring data privacy, its growing reliability indicates a transformation and improvement in the field of healthcare.