• There are people who research and develop new algorithms and architectures, advancing the field of artificial intelligence.
• There are people who train the neural network models such as OpenAI’s ChatGPT, Qwen, and DeepSeek models, pushing the boundaries of what AI can achieve.
• There are people who use these AI models to transform and disrupt existing businesses, creating innovative solutions and redefining industries.

Majority of them would be of the third type: use AI models to transform and disrupt existing business. Currently, there are surge of coding assistant tools such as copilot, cursor, windsurf, trae etc., how do they do it?

Core Components of Development

Here’s a breakdown of how they are created:

(a) Training the Language Model

1. Pretraining on Large Datasets:
   • The LLMs are pretrained on massive datasets, including publicly available code repositories, documentation, tutorials, and forums like GitHub, Stack Overflow, and others.
   • These datasets help the models learn the syntax, semantics, and patterns of different programming languages.
2. Fine-tuning for Programming Tasks:
   • After pretraining, the model is fine-tuned on curated, high-quality code datasets to improve its understanding of specific tasks like function completion, debugging, or generating boilerplate code.
   • Fine-tuning also helps reduce irrelevant or unsafe outputs.

(b) Integration with Development Environments (IDEs)

• IDE Plugins/Extensions:
  • Both tools are integrated into popular IDEs (e.g., VS Code, JetBrains IDEs) as plugins/extensions. This requires building APIs that communicate seamlessly between the IDE and the AI backend.
• Context Awareness:
  • The tools use the current context of the file or function being edited to make suggestions. This involves sending snippets of code, cursor position, and file structure to the AI model.

(c) Intelligent Code Completion

• Code Suggestions:
  • The AI predicts the next lines of code based on the current context (e.g., function signatures, inline comments, imports).
  • Example: While writing a Python function, Copilot can suggest the next logical line based on common patterns in similar code.
• Interactive Assistance:
  • Cursor goes beyond simple suggestions by allowing programmers to ask questions (e.g., “Refactor this code”) or debug specific issues interactively.

(d) Handling Large Contexts

• Tools like Copilot and Cursor often use strategies to handle large files or multi-file projects:
  • Extended Context Windows: Using LLMs with larger context sizes (like OpenAI’s GPT-4 or custom implementations).
  • Code Chunking: Breaking down large files into manageable chunks and processing them efficiently.

(e) Reinforcement Learning from User Feedback (RLHF)

• These tools use Reinforcement Learning with Human Feedback to continuously improve:
  • Developers provide implicit feedback (e.g., accepting or rejecting a suggestion).
  • Explicit feedback (e.g., thumbs up/down or corrections) helps fine-tune the model.

(f) Safety Mechanisms

• Built-in filters detect potentially harmful or insecure code (e.g., injection attacks or hardcoded credentials).
• These mechanisms minimize the risk of generating malicious code.

Clearly, you need to master coding even with help of AI to build up such a system. Now let’s try to build up a healthcare assistant tool.

1. Key Objectives

The tool should:

• Take patient symptoms, history, or test results as input.
• Provide diagnostic suggestions based on clinical knowledge.
• Recommend possible next steps (e.g., tests or specialist referrals).
• Support doctors in decision-making (e.g., differential diagnosis).

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2. Software Architecture

Components:

1. Frontend (User Interface):
   • User-friendly app or web portal for doctors or patients.
   • Inputs: Symptoms, patient history, lab test results.
   • Outputs: Diagnostic suggestions and recommendations.
2. Backend:
   • Core logic and AI model processing.
3. LLM Integration:
   • Fine-tuned LLM trained on medical datasets (e.g., clinical notes, symptom libraries, medical research papers).
4. Knowledge Base:
   • Up-to-date medical information for accuracy.
   • Integration with public or licensed datasets like PubMed or MIMIC-III.
5. Feedback Loop:
   • Reinforcement learning to improve the model from user feedback.

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3. Leveraging LLM

LLM’s Role:

• Natural Language Understanding (NLP): Interprets patient input (e.g., “I have a headache and fever”).
• Reasoning: Suggests potential diagnoses (e.g., “Possible influenza or sinus infection”).
• Summarization: Synthesizes medical histories and test results into concise summaries.
• Context Awareness: Understands comorbidities and patient-specific details to refine suggestions.

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4. Development Workflow

Here’s how to emulate Copilot-like AI for healthcare diagnosis:

Step 1: Choose or Train an LLM

• Use a general-purpose model (e.g., OpenAI’s GPT-4, LLaMA 2, or Anthropic’s Claude) fine-tuned for healthcare tasks.
• Fine-tune the model on:
  • Symptom datasets.
  • Clinical guidelines.
  • Electronic Health Records (EHR) (anonymized and compliant with privacy laws).
  • Diagnostic codes (e.g., ICD-10).

Step 2: Build Context Handling for Medical Inputs

• Process multi-modal inputs (text, lab results, imaging data).
• Example Input Context:vbnetCopyEditPatient Symptoms: "Headache, fever, nausea." History: "Diabetic, hypertensive, allergic to penicillin." Lab Results: WBC count elevated.
• Use embeddings to capture patient data contextually.

Step 3: Integrate with Medical Knowledge Base

• API Integration with:
  • UpToDate for clinical guidelines.
  • SNOMED-CT/ICD for diagnostic classification.
  • Lab Test Reference Ranges for test result interpretation.

Step 4: Develop Frontend Features

• Input Suggestions: Autocomplete for symptoms or medical terms.
• Interactive Diagnostic Flow: Suggest follow-up questions (e.g., “Is the headache localized or general?”).
• Interactive Results: Show ranked probabilities for diagnoses.

Step 5: Add Feedback and Reinforcement Learning

• Collect user feedback to refine the AI:
  • Accepted/rejected suggestions.
  • Outcomes of the proposed diagnosis.

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5. Sample Code for Core Functionality

Below is a simplified Python-based example for a diagnostic assistant using Hugging Face and a custom fine-tuned model:

(a) Load the Fine-Tuned LLM

pythonCopyEditfrom transformers import AutoTokenizer, AutoModelForCausalLM

# Load a fine-tuned medical language model
model_name = "fine_tuned_healthcare_model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

---

(b) Define the Diagnostic Assistant Function

pythonCopyEditdef healthcare_assistant(prompt, patient_data, max_length=200):
    """
    Generate a diagnostic suggestion based on symptoms and history.
    """
    # Combine prompt with patient data
    input_prompt = f"Patient Data:\n{patient_data}\n\nPrompt:\n{prompt}"
    
    # Tokenize and generate response
    inputs = tokenizer(input_prompt, return_tensors="pt")
    outputs = model.generate(inputs["input_ids"], max_length=max_length)
    
    # Decode the model output
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

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(c) Example Usage

pythonCopyEdit# Input patient data
patient_data = """
Symptoms: Headache, fever, nausea.
Medical History: Diabetes, hypertension, allergic to penicillin.
Lab Results: Elevated WBC count, normal platelet levels.
"""

# Diagnostic query
prompt = "What are the possible diagnoses and recommended next steps?"

# Generate AI-driven suggestion
diagnosis = healthcare_assistant(prompt, patient_data)
print("AI Diagnostic Suggestion:\n", diagnosis)

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6. Features to Add

• Explainability:
  • Highlight why the AI suggested a particular diagnosis (e.g., “Based on fever and elevated WBC, infection is likely”).
• Visualization:
  • Show likelihoods of different diagnoses in a chart.
• Risk Assessment:
  • Highlight high-risk conditions based on patient input.

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7. Deployment and Compliance

Deployment:

• Use cloud services (e.g., AWS, GCP, or Azure) for scalability.
• Deploy as an API to allow integration with hospital systems.

Compliance:

• Ensure HIPAA (USA), GDPR (Europe), or other healthcare privacy law compliance.
• Keep all patient data anonymized and encrypted.

---

8. Example Output

Input:

yamlCopyEditSymptoms: Headache, fever, nausea.
Medical History: Diabetic, hypertensive.
Lab Results: WBC count elevated.

Output:

markdownCopyEditPossible Diagnoses:
1. Influenza (High probability)
2. Sinus Infection (Moderate probability)
3. Meningitis (Low probability - further investigation recommended)

Recommended Next Steps:
- Prescribe antipyretic medication for fever.
- Conduct a nasal swab for influenza confirmation.
- Consider CT scan if symptoms persist.

One key know-how is how to fine-tune model with domain-specific data? Another one is how to handle long context in an interactive fashion? These two questions worth separate blogs to elaborate.

And try to dedicate resources/energy to accomplish these key know-how!