The AI-Powered Symptom Checker Agent is a next-generation interactive healthcare assistant that utilizes patient symptoms understanding, patient questioning for clarification, and in the end, generates medical assessments in a structured format. Its primary function is to simplify patient triage with the help of AI technologies, and it also facilitates the patient to doctor escalation for a detailed evaluation if needed.

This is a platform whereby hospitals, clinics, health start-ups, and tele-medicine facilities can avail themselves of the service of a faster, safer, and well-structured tool for symptom-based consultation.

Introduction

We created an AI-driven symptom checker agent that comprehends human-readable patient descriptions of symptoms and applies medical logic to answer. The system relies on a powerful LLM — Llama3-OpenBioLLM-70B — to provide medically cautious and structured results.

The agent is conversational, it takes grammatically correct sentences and relevantly asks questions, and finally generates a brief medical report which identifies the symptoms, gives the initial assessment, lists the red flags, and recommends the patient to the hospital or doctor's clinic.

The product is intended for doctors, clinics, and digital health platforms as a tool that elevates patient engagement and frees doctors' time for other tasks.

Challenges We Identified

Before we came up with this innovative solution, we observed that the same issues were repeatedly present in different symptom-reporting workflows:

• Unstructured patient inputs: Patients often provide symptoms in a confusing and incomplete way, making triage slow and prone to mistakes.
• Manual triage was not scalable: Doctors and support teams had to keep asking clarifying questions, which increased administrative workload.
• Lack of consistent medical documentation: Most systems could not generate structured and easily understandable medical summaries for doctors to quickly get an idea and decide on the next steps.
• High risk of misinterpretation: Without a proper series of questions, it cannot be ensured that all important aspects will be considered, and some red flags may be missed.
• No easy doctor escalation: Patients lacked a simple way of having serious cases escalated to be checked by professionals.

Our Strategic Shift

We implemented a completely AI-automated medical triage system to address those challenges. Our system now:

• Employs LLM-driven thinking to come up with a maximum of five questions for clarification.
• Automatically generates standardized and structured medical reports.
• Ensures interaction safety and medical caution with the help of models trained on domain data.
• Enables continuous patient conversations bound by sessions with the help of UUID tracking.
• Facilitates a simple and quick doctor escalation when the necessity arises.

This approach guarantees correctness, organization, and trust in the whole diagnosing workflow.

Benefits of the In-House Solution

1. Faster triage: The AI immediately understands the symptoms and asks for further information if necessary, removing the need for manual intake staff.
2. Accurate and context-aware medical reasoning: OpenBioLLM-70B is different from normal chatbots as it takes medical context into account and abides by a strict prompting strategy.
3. Structured medical reports: At the end of each interaction, the system generates a clear report that includes:
   • Symptoms summary
   • Preliminary assessment
   • Recommendations
   • Red flags
4. Supports doctor escalation: The patient is given the option of having a doctor check their case whenever they want.
5. Private and secure sessions: Environment variables, secure APIs, and tightly controlled LLM calls work together to provide patients with the privacy they deserve.
6. Easy to use: A simple Streamlit UI allows patients to interact with the system naturally, see the reports, and escalate the issue with just one click.

Technology Stack Used

Frontend: Streamlit was used for creating a neat, interactive UI for patients.

Backend: FastAPI is used for handling endpoints, sessions, and LLM interactions.

LLM: Llama3-OpenBioLLM-70B through Hugging Face Inference Router.

Config and security: dotenv is utilized for managing API keys and securing configuration.

Other tools: Python, Requests, and UUID session tracking.

Rapid Build Strategies

We adopted the following strategies to quickly and reliably build this system:

• Context-aware prompting optimized for safe medical responses.
• Reusable FastAPI modules for session management and report generation.
• Streamlit components for seamless patient interaction.
• Hugging Face Router integration for scalable LLM inference.
• Cloud-based testing for latency and reliability.
• Lightweight in-memory storage for fast development cycles.

The result: a fully functional medical triage prototype built efficiently without sacrificing safety or accuracy.

Demonstration of Patient Workflow

Conclusion

The AI-Powered Symptom Checker Agent by Codework is an example of how AI-structured triage can make the healthcare system leaner and safer for patients. The collaboration of Streamlit, FastAPI, and sophisticated LLM models makes the solution accurate, speedy, and user-friendly. By the addition of features such as database integration, multilingual capabilities, and real doctor connectivity, this platform will be the stepping stone of digital healthcare automation of the ​‍​‌‍​‍‌​‍​‌‍​‍‌future.