The medical industry sets a uniquely high bar for accuracy and compliance in scenarios such as consultations, prescriptions, and rehabilitation guidance. Yet doctor‑patient communication still relies heavily on manual effort, which limits scalability and places a significant burden on physicians. For online healthcare providers, this creates a barrier to growth. At the same time, traditional AI models struggle with the complexity of medical texts, cases, and prescriptions, leading to weak generalization ability. Patients also face clunky digital interfaces, resulting in poor consultation experiences and low retention on medical platforms.
Our Solution: Fine‑Tuned Medical Large Models with Digital Human Interaction
We addressed these challenges by fine‑tuning large models on proprietary medical data, ensuring that answers meet strict accuracy and professional standards. To improve patient engagement, we introduced a digital human front‑end that enables natural, lifelike consultations between patients and AI. The system supports key medical use cases—including medical record analysis, drug guidance, and rehabilitation follow‑up—while seamlessly integrating with backend systems managing prescriptions and medical records. Deployed in a compliance‑ready environment, the architecture safeguards patient data and meets healthcare industry regulations for security and privacy.
Application Impact: Accurate, Scalable, Patient‑Centric
The solution reduced doctors’ workload by nearly half, alleviating communication burdens and freeing medical staff for higher‑value tasks. Patient waiting times for online consultation were cut by 60%, raising service levels dramatically. Diagnosis accuracy improved through model fine‑tuning, while patient satisfaction and retention rates for the online medical platform grew steadily. Most importantly, the deployment has established a standardized “digital human + medical large model” delivery model, which can be expanded rapidly into broader healthcare scenarios.