For AI models to deliver reliable healthcare recommendations, they require accurate, structured, and diverse datasets. Yma integrates patient-reported data, clinical records, and external medical studies to create a comprehensive AI-driven medical intelligence system.
From Patients:
From Healthcare Providers:
Our approach to curating and training AI models relies on selecting high-quality, domain-relevant content that aligns with the following principles:
1️⃣ Domain Relevance All articles must pertain to the specific domain or field being addressed. They should provide accurate, contextually appropriate information tailored to the intended audience.
2️⃣ Evidence-Based Content We prioritize articles grounded in the strongest possible evidence. This includes peer-reviewed studies, systematic reviews, clinical guidelines, and meta-analyses to ensure reliability and scientific validity.
3️⃣ Comprehensive Language Articles must be written in a clear, concise, and comprehensive manner. The language should be accessible to diverse audiences, allowing both professionals and laypersons to understand the content effectively.
4️⃣ Tone of Voice The tone of voice used in the training content is an essential component of our model's development. We teach our AI to adopt an empathetic, professional, and user-friendly tone to enhance communication and engagement.
A knowledge database in a specific domain is continuously updated with the latest scientific advancements. Our approach focuses on manually curating and regularly updating high-quality data.
Before gathering references for model development, a team of healthcare professionals and biomedical research experts defines the key aspects of the domain. For each aspect, we compile a detailed list of relevant topics, ensuring comprehensive coverage with high-evidence information.