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Demystifying CrewAI: Building a Collaborative AI Healthcare Assistant

  • Writer: Sangamesh Gella
    Sangamesh Gella
  • May 1
  • 5 min read

Updated: May 6

Artificial Intelligence (AI) is rapidly transforming healthcare, but the magic truly happens when multiple AI agents work together, each specialising in a unique aspect of patient care. In this blog, we'll explore how Crew AI enables this collaboration and walk through a practical Python example that brings these concepts to life for everyone, regardless of their technical background.

crew ai logo
CrewAI Logo

What is CrewAI?

Crew AI is a framework for building multi-agent AI systems. Instead of relying on a single, monolithic AI, Crew AI lets you create a "crew" of specialised agents, each with its own expertise and role. These agents communicate, share context, and solve complex problems together, much like a team of healthcare professionals.


How Does the Example Healthcare Crew Work?

Let's break down the code and the concepts behind it, step by step.


1. Collecting Patient Information

The first part of the code is a simple, interactive form that gathers patient details:

  1. Name,

  2. Age,

  3. Gender,

  4. Symptoms,

  5. Medical History,

  6. Current Medications and

  7. Allergies


This information is crucial for any healthcare decision and is collected via command-line prompts. The script ensures valid input (e.g., age must be a number) and confirms the data with the user before proceeding.


2. Introducing the Crew: Specialised AI Agents

Each AI agent in Crew AI is like a member of a healthcare team, with a clear role:

Agent Role

Responsibility

Medical Diagnostician

Analyses symptoms and history to suggest possible diagnoses

Medical Guidelines Expert

Finds and interprets the latest medical guidelines relevant to the case

Treatment Plan Specialist

Develops a personalised treatment plan, considering patient specifics and best practices

Patient Education Specialist

Explains medical information in clear, patient-friendly language

Each agent is powered by a language model, such as GPT-4o-mini, and some have access to external tools like Serper for research.

AI Agents solving the diagnosis for a patient
AI Agents solving the diagnosis for a patient

3. Assigning Tasks and Workflow

Tasks are defined for each agent, specifying what they're expected to do. For example:


  1. The Diagnostician receives the patient's info and outputs possible diagnoses, ranked by likelihood.

  2. The Guidelines Expert researches best practices for those diagnoses.

  3. The Treatment Planner crafts a tailored treatment plan.

  4. The Patient Educator explains everything in plain language.


Context Sharing: Some tasks depend on the outputs of others. For instance, the treatment plan is based on both the diagnosis and the latest guidelines. This mirrors real-world teamwork in healthcare.


4. Orchestrating the Crew

The agents and their tasks are bundled into a Crew object. The process is set to "sequential," meaning each agent works in turn, passing context to the next, just like a relay race.




When the process_patient function is called, it:

  • Updates the diagnosis task with the specific patient info

  • Runs the crew, kicking off the chain of collaboration

  • Returns the final output: a comprehensive, AI-generated analysis and recommendation


5. Putting It All Together

The main script (patient_input.py) handles user interaction and calls the crew for analysis. The core AI logic is separated in main.py, making the code modular and reusable.

patient_input.py

def get_patient_info():
    print("Patient Information Entry Form")
    print("-----------------------------")
    
    patient_info = {}
    patient_info["name"] = input("Please enter patient name: ")
    
    # For age, ensure we get a valid integer
    while True:
        try:
            patient_info["age"] = int(input("Please enter patient age: "))
            break
        except ValueError:
            print("Please enter a valid number for age.")
    
    patient_info["gender"] = input("Please enter patient gender: ")
    patient_info["symptoms"] = input("Please enter patient symptoms: ")
    patient_info["medical_history"] = input("Please enter patient medical history: ")
    patient_info["medications"] = input("Please enter current medications: ")
    patient_info["allergies"] = input("Please enter allergies: ")
    
    # Display the entered information for confirmation
    print("\nPatient Information Summary:")
    for key, value in patient_info.items():
        print(f"{key.capitalize()}: {value}")
    
    confirm = input("\nIs this information correct? (y/n): ")
    if confirm.lower() != 'y':
        print("Let's try again.")
        return get_patient_info()
    
    return patient_info

if __name__ == "__main__":
    # Import process_patient from main.py
    from main import process_patient
    
    # Get patient information
    patient_data = get_patient_info()
    
    # Process the patient data using the function from main.py
    print("\nProcessing patient information...")
    result = process_patient(patient_data)
    
    # Display the result
    print("\nAI Analysis Result:")
    print(result)

Why Is This Approach Powerful?

  • Specialisation: Each agent is an expert in its domain, leading to more accurate and nuanced results.

  • Transparency: Tasks and agent roles are clearly defined, making it easier to understand and trust the AI's decisions.

  • Extensibility: New agents (e.g., a billing expert, a follow-up coordinator) can be added as needed.

  • Patient-Centric: The final output isn't just clinical- it includes patient education, empowering individuals to understand their health.


A Glimpse Into the Future

Imagine this system integrated into a hospital's workflow. Nurses enter patient data, the AI crew provides a preliminary analysis, doctors review and adjust the recommendations, and patients receive clear explanations—all in just minutes.

Crewai isn't just about automation; it's about collaboration between humans and AI agents as well as among AI agents themselves.


Conclusion

By breaking down complex healthcare tasks into specialised roles and fostering teamwork among AI agents, Crewai offers a blueprint for building smarter, more transparent, and patient-friendly AI systems. Whether you're a developer, a healthcare professional, or simply curious about AI, this approach makes the future of medicine more accessible and understandable to everyone.


Ready to try it yourself? Clone the code, define your agents, and see how Crewai can revolutionise any workflow!

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