Our Process

In our pursuit of developing a cutting-edge solution for medical diagnostics, we embarked on a two-fold strategy to harness the power of natural language processing.

Initially, we carefully selected a lightweight large language model (LLM) that had already undergone pre-training on a vast corpus of diverse text, encompassing billions of parameters. This pre-trained model exhibited a remarkable understanding of language and could generate coherent responses to a wide array of queries. However, recognizing the unique demands of our medical diagnostics use case, we understood the significance of fine-tuning the model to ensure its seamless integration with our specific requirements.

To accomplish this, we meticulously curated a comprehensive dataset comprising medical diagnostics information, featuring an array of symptoms, conditions, and diagnostic insights. This dataset served as the bedrock for the fine-tuning process, providing the model with an accurate and tailored source of data to draw upon during its learning phase.

Through fine-tuning, we sought to imbue the model with domain-specific knowledge, enabling it to grasp intricate medical terminologies, identify subtle symptomatology, and deliver precise and contextually relevant diagnostic information. The convergence of a pre-trained lightweight LLM model with a targeted medical diagnostics dataset served as the cornerstone of our endeavor, empowering us to provide a sophisticated and trustworthy AI-driven solution for medical practitioners and patients alike.