Document Processing
The team faced the challenge of integrating various data types seamlessly, as the AI needed to process both speech and data from the patients' Electronic Health Records (EHR).
Reported impact
With 1 million healthcare practitioners and 90 million patients using their platform across Europe, the company is now at the forefront of AI to go one step further in delivering on their promise.
Doctolib built its Microsoft solution to improve document-heavy expert workflows. The team faced the challenge of integrating various data types seamlessly, as the AI needed to process both speech and data from the patients' Electronic Health Records (EHR). Key components included Azure OpenAI Service ran model inference or heavy processing, and Azure AI Studio coordinated workflows, prompts, or deployment. User prompts and domain content are orchestrated through Azure AI Studio, processed by Azure OpenAI Service, and then delivered through Azure OpenAI Service so Doctolib can return context-aware answers or automation in the flow of work. The stack stayed governed and extensible on Microsoft. With 1 million healthcare practitioners and 90 million patients using their platform across Europe, the company is now at the forefront of AI to go one step further in delivering on their promise.
User prompts and domain content are orchestrated through Azure AI Studio, processed by Azure OpenAI Service, and then delivered through Azure OpenAI Service so Doctolib can return context-aware answers or automation in the flow of work.
Medical consultations are now summarized in 15 seconds, allowing practitioners to spend twice as much time with patients.
The assistant transcribes the consultation in real-time and turns it into a structured, relevant, comprehensible summary within only 15 seconds.
The company leverages Microsoft technologies to develop an AI-powered medical assistant, integrating both Azure OpenAI Service and Mistral Large on Azure.
The team faced the challenge of integrating various data types seamlessly, as the AI needed to process both speech and data from the patients' Electronic Health Records (EHR).
With 1 million healthcare practitioners and 90 million patients using their platform across Europe, the company is now at the forefront of AI to go one step further in delivering on their promise.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
Microsoft Sample Jul 25, 2026 Brown Health As Brown Health expanded its regional footprint, clinicians faced rising patient demand, financial pressures, labor shortages, burnout, and heavy documentation demands that made it increasingly difficult to dedicate… Burnout rates among physicians in some specialties can be 40% or more. Dragon Copilot · Microsoft 365 Copilot · Microsoft Copilot Studio Microsoft Sample Feb 4, 2025 Ontada It is time-consuming, error-prone, and cost-prohibitive for Ontada to generate key insights using manual processes to analyze 150 million unstructured oncology documents. It is time-consuming, error-prone, and cost-prohibitive for Ontada to generate key insights using manual… Azure AI Document Intelligence · Azure AI Foundry · Azure AI Services Microsoft Sample Jan 28, 2025 amaise amaise was founded in Switzerland in 2019. Its core mission? To tackle one central challenge in the healthcare and insurance sector: the need to process unstructured medical case histories. The data comes from all kinds… Collaboration with Baloise, one of Switzerland's biggest insurers, illustrates how amaise is revolutionizing… Azure OpenAI Service 