Kye Gomez
1 month ago
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# Medical Swarm Example
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1. Get your API key from the Swarms API dashboard [HERE](https://swarms.world/platform/api-keys)
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2. Create a `.env` file in the root directory and add your API key:
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```bash
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SWARMS_API_KEY=<your-api-key>
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```
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3. Create a Python script to create and trigger the medical swarm:
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```python
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import os
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import requests
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from dotenv import load_dotenv
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import json
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load_dotenv()
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# Retrieve API key securely from .env
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API_KEY = os.getenv("SWARMS_API_KEY")
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BASE_URL = "https://swarms-api-285321057562.us-east1.run.app"
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# Headers for secure API communication
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headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}
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def create_medical_swarm(patient_case: str):
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"""
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Constructs and triggers a full-stack medical swarm consisting of three agents:
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Diagnostic Specialist, Medical Coder, and Treatment Advisor.
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Each agent is provided with a comprehensive, detailed system prompt to ensure high reliability.
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"""
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payload = {
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"swarm_name": "Enhanced Medical Diagnostic Swarm",
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"description": "A swarm of agents specialized in performing comprehensive medical diagnostics, analysis, and coding.",
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"agents": [
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{
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"agent_name": "Diagnostic Specialist",
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"description": "Agent specialized in analyzing patient history, symptoms, lab results, and imaging data to produce accurate diagnoses.",
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"system_prompt": (
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"You are an experienced, board-certified medical diagnostician with over 20 years of clinical practice. "
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"Your role is to analyze all available patient information—including history, symptoms, lab tests, and imaging results—"
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"with extreme attention to detail and clinical nuance. Provide a comprehensive differential diagnosis considering "
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"common, uncommon, and rare conditions. Always cross-reference clinical guidelines and evidence-based medicine. "
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"Explain your reasoning step by step and provide a final prioritized list of potential diagnoses along with their likelihood. "
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"Consider patient demographics, comorbidities, and risk factors. Your diagnosis should be reliable, clear, and actionable."
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),
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"model_name": "openai/gpt-4o",
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"role": "worker",
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"max_loops": 2,
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"max_tokens": 4000,
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"temperature": 0.3,
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"auto_generate_prompt": False
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},
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{
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"agent_name": "Medical Coder",
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"description": "Agent responsible for translating medical diagnoses and procedures into accurate standardized medical codes (ICD-10, CPT, etc.).",
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"system_prompt": (
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"You are a certified and experienced medical coder, well-versed in ICD-10, CPT, and other coding systems. "
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"Your task is to convert detailed medical diagnoses and treatment procedures into precise, standardized codes. "
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"Consider all aspects of the clinical documentation including severity, complications, and comorbidities. "
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"Provide clear explanations for the codes chosen, referencing the latest coding guidelines and payer policies where relevant. "
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"Your output should be comprehensive, reliable, and fully compliant with current medical coding standards."
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),
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"model_name": "openai/gpt-4o",
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"role": "worker",
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"max_loops": 1,
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"max_tokens": 3000,
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"temperature": 0.2,
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"auto_generate_prompt": False
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},
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{
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"agent_name": "Treatment Advisor",
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"description": "Agent dedicated to suggesting evidence-based treatment options, including pharmaceutical and non-pharmaceutical interventions.",
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"system_prompt": (
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"You are a highly knowledgeable medical treatment specialist with expertise in the latest clinical guidelines and research. "
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"Based on the diagnostic conclusions provided, your task is to recommend a comprehensive treatment plan. "
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"Your suggestions should include first-line therapies, potential alternative treatments, and considerations for patient-specific factors "
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"such as allergies, contraindications, and comorbidities. Explain the rationale behind each treatment option and reference clinical guidelines where applicable. "
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"Your recommendations should be reliable, detailed, and clearly prioritized based on efficacy and safety."
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),
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"model_name": "openai/gpt-4o",
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"role": "worker",
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"max_loops": 1,
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"max_tokens": 5000,
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"temperature": 0.3,
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"auto_generate_prompt": False
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}
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],
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"max_loops": 3,
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"swarm_type": "Sequential" # Sequential workflow: Each agent builds on the output of the previous one
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}
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# Payload includes the patient case as the task to be processed by the swarm
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payload = {
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"task": patient_case,
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"swarm": payload
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}
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response = requests.post(
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f"{BASE_URL}/swarm/completion",
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headers=headers,
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json=payload,
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)
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if response.status_code == 200:
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print("Swarm successfully executed!")
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return json.dumps(response.json(), indent=4)
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else:
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print(f"Error {response.status_code}: {response.text}")
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return None
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# Example Patient Task for the Swarm to diagnose and analyze
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if __name__ == "__main__":
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patient_case = (
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"Patient is a 55-year-old male presenting with severe chest pain, shortness of breath, elevated blood pressure, "
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"nausea, and a family history of cardiovascular disease. Blood tests show elevated troponin levels, and EKG indicates ST-segment elevations. "
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"The patient is currently unstable. Provide a detailed diagnosis, coding, and treatment plan."
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)
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diagnostic_output = create_medical_swarm(patient_case)
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print(diagnostic_output)
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```
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