From Lab Results to Appointments: Building an Autonomous AI Physician Assistant with AutoGPT

A developer built an autonomous AI physician assistant using the AutoGPT protocol, OpenAI API, and SerpApi to transform raw medical reports into actionable healthcare roadmaps. The agent uses a feedback loop to research medical guidelines, cross-reference hospital departments, and suggest follow-up plans, outputting structured data via Pydantic models.

We’ve all been there: you get your annual health check-up report, and it’s filled with cryptic terms like "Hyperechoic focus" or "Elevated ALT levels." Naturally, you head to Google, only to convince yourself you have three days to live. 😅 What if we could build an Autonomous AI Agent that doesn't just define these terms, but actually researches the latest medical guidelines, cross-references hospital departments, and suggests a concrete follow-up plan? In this tutorial, we are diving deep into Autonomous Agents and Medical Automation . We’ll be using the AutoGPT protocol , OpenAI API , and SerpApi to build an AI Physician Assistant that transforms raw medical data into actionable healthcare roadmaps. Unlike a simple chatbot, an autonomous agent uses a feedback loop. It observes the data, decides which tool to use search or analyze , executes the action, and iterates until the goal is met. php graph TD subgraph Input Layer A Raw Medical Report -- B Pydantic Data Parser end subgraph Agent Core AutoGPT Logic Loop B -- C{Reasoning Engine} C -- |Need Knowledge| D SerpApi: Medical Encyclopedia C -- |Need Logistics| E SerpApi: Hospital Schedules D -- C E -- C end subgraph Output Layer C -- F Term Interpretation C -- G Department Recommendations C -- H Final Action Plan end F & G & H -- I User To follow this advanced guide, you’ll need: We don't want the AI to hallucinate or return messy JSON. By using Pydantic , we enforce a strict structure on how the agent interprets the health report. python from pydantic import BaseModel, Field from typing import List, Optional class MedicalFinding BaseModel : term: str = Field ..., description="The medical term found in the report" status: str = Field ..., description="Normal, Borderline, or Abnormal" interpretation: str = Field ..., description="Plain-english explanation" suggested department: str = Field ..., description="Which hospital department to visit" class HealthActionPlan BaseModel : summary: str findings: List MedicalFinding urgency score: int = Field ..., ge=1, le=10, description="1 is routine, 10 is ER" The core power of an AutoGPT-style agent lies in its ability to browse the web. We’ll use SerpApi to allow the agent to look up specific medical terms and local hospital availability. python import os from serpapi import GoogleSearch def medical search tool query: str : """Searches for medical definitions and hospital departments.""" params = { "q": f"medical definition and department for {query}", "location": "New York, United States", "hl": "en", "gl": "us", "api key": os.getenv "SERP API KEY" } search = GoogleSearch params results = search.get dict return results.get "organic results", 0 .get "snippet", "No info found." Now, we build the agent. We use a "Chain of Thought" prompt that forces the agent to use the medical search tool before giving a final answer. python import openai def ai physician assistant raw report text: str : system prompt = """ You are an AI Physician Assistant. 1. ANALYZE the report for abnormalities. 2. SEARCH for any terms you aren't 100% sure about using the search tool. 3. MATCH findings to the correct medical department e.g., Cardiology, Endocrinology . 4. OUTPUT a JSON object following the HealthActionPlan schema. """ In a real AutoGPT implementation, this would be a loop. For brevity, we'll use OpenAI's Function Calling/Tools API. response = openai.chat.completions.create model="gpt-4o", messages= {"role": "system", "content": system prompt}, {"role": "user", "content": f"Analyze this report: {raw report text}"} , tools= { "type": "function", "function": { "name": "medical search tool", "parameters": { "type": "object", "properties": {"query": {"type": "string"}} } } } , tool choice="auto" return response.choices 0 .message While this script is a great starting point for "learning in public," building a production-ready medical AI requires more robust error handling, HIPAA-compliant data masking, and sophisticated RAG Retrieval-Augmented Generation pipelines. For more production-ready patterns and advanced architectural insights on autonomous agents, I highly recommend checking out the WellAlly Blog . It’s a fantastic resource for developers looking to move beyond "Hello World" scripts and into scalable AI infrastructure. When we pass a report like "Triglycerides: 250 mg/dL, Thyroid Stimulating Hormone: 6.2 mIU/L" to our agent, it doesn't just say "they are high." The agent will: { "summary": "Your report shows signs of mild hypothyroidism and high cholesterol.", "findings": { "term": "TSH 6.2 mIU/L", "status": "Abnormal", "interpretation": "Your thyroid is slightly underactive.", "suggested department": "Endocrinology" } , "urgency score": 4 } Building with the AutoGPT protocol allows us to create software that doesn't just process data, but solves problems . By combining the reasoning of GPT-4o with the real-time capabilities of SerpApi , we’ve built a tool that can significantly reduce the anxiety of reading medical reports. What do you think? Should AI agents be used for initial medical triage, or is the risk of hallucination still too high? Let me know in the comments 👇 Disclaimer: This is a technical demonstration. Always consult with a human doctor for medical advice.