AI improved coding accuracy and efficiency, reducing processing time and ensuring optimal reimbursement. By decreasing incorrect code attribution, it enhanced quality assurance, leading to better patient outcomes and faster, more accurate billing decisions.

AI integration improved diagnostic accuracy, reduced system strain, and sped up decision-making. By leveraging patient history and similar case data, it enhanced treatment success, caught severe cases early, and reduced unnecessary delays, improving overall healthcare outcomes.

AI-driven demand forecasting optimized hospital resources, reducing wait times and ensuring adequate staffing. It enabled better preparedness for high-demand periods, improved patient care, and contributed to cost savings and more effective long-term planning.

The AI-powered solution transcribed real-time conversations between patients and physicians, automatically generating reports based on conclusions from the conversation. This streamlined documentation, reduced administrative workload for doctors, and improved patient experience.

AI improved therapy treatment accuracy by over 90%, leveraging large language models for medical applications. The solution also supported the evaluation of AI models within European data protection regulations and helped build Azure Cloud infrastructure for future use case expansions.