Revolutionizing Healthcare with Phi-3-Vision: Automating Diagnosis and Treatment Recommendations

In the ever-evolving landscape of healthcare, technology continues to play a pivotal role in enhancing patient care and improving clinical outcomes. Among the latest advancements, Phi-3-Vision stands out as a groundbreaking multimodal model, combining text and image analysis with exceptional reasoning capabilities. This innovative technology is set to transform the healthcare industry, particularly in the realm of automated diagnosis and treatment recommendations.

Unleashing the Power of Phi-3-Vision in Healthcare

Phi-3-Vision is designed to analyze real-world images, extract and reason over text from images, and generate insights with remarkable precision. By leveraging this model, we can create an intelligent system that not only analyzes medical images but also integrates textual information from patient reports to provide comprehensive and accurate diagnoses.

Project Showcase: Intelligent Medical Image Analysis and Report Generation

Project Overview

Imagine a system that can seamlessly upload medical images and text reports, analyze them, and generate detailed diagnosis reports along with potential treatment recommendations. This project aims to achieve exactly that, harnessing the power of Phi-3-Vision to revolutionize medical image analysis.

Key Objectives

1. Image and Text Integration: Develop a system capable of uploading and processing medical images and text reports.
2. Advanced Analysis: Utilize Phi-3-Vision to detect abnormalities in medical images and extract crucial information from text reports.
3. Comprehensive Reporting: Generate detailed diagnosis reports and suggest treatment options based on integrated analysis.
4. Doctor Interface: Provide an intuitive interface for doctors to review and validate the generated reports.

Technical Implementation

Frontend Development: A user-friendly web interface built with React.js allows users to upload images and text reports easily.

import React, { useState } from 'react';
import axios from 'axios';

function App() {
  const [image, setImage] = useState(null);
  const [text, setText] = useState('');
  const [report, setReport] = useState('');

  const handleImageChange = (e) => {
    setImage(e.target.files[0]);
  };

  const handleTextChange = (e) => {
    setText(e.target.value);
  };

  const handleSubmit = async () => {
    const formData = new FormData();
    formData.append('image', image);
    formData.append('text', text);

    const response = await axios.post('/api/analyze', formData);
    setReport(response.data.report);
  };

  return (
    <div>
      <h1>Medical Image Analysis</h1>
      <input type="file" onChange={handleImageChange} />
      <textarea value={text} onChange={handleTextChange} />
      <button onClick={handleSubmit}>Analyze</button>
      <pre>{report}</pre>
    </div>
  );
}

export default App;        

Backend Development: A robust backend using Python with Flask to handle image and text analysis.

from flask import Flask, request, jsonify
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image
import requests

app = Flask(__name__)
model_id = "microsoft/Phi-3-vision-128k-instruct"

model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto")
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

@app.route('/api/analyze', methods=['POST'])
def analyze():
    image = request.files['image']
    text = request.form['text']

    messages = [
        {"role": "user", "content": "What is shown in this image?"},
        {"role": "assistant", "content": "The chart displays the percentage of respondents who agree with various statements about their preparedness for meetings. It shows five categories: 'Having clear and pre-defined goals for meetings', 'Knowing where to find the information I need for a meeting', 'Understanding my exact role and responsibilities when I'm invited', 'Having tools to manage admin tasks like note-taking or summarization', and 'Having more focus time to sufficiently prepare for meetings'. Each category has an associated bar indicating the level of agreement, measured on a scale from 0% to 100%."},
        {"role": "user", "content": "Provide insightful questions to spark discussion."}
    ]

    image = Image.open(requests.get(image, stream=True).raw)
    prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

    inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0")

    outputs = model.generate(**inputs, max_new_tokens=500, temperature=0.0)
    generated_text = processor.batch_decode(outputs, skip_special_tokens=True)

    return jsonify({'report': generated_text})

if __name__ == '__main__':
    app.run(debug=True)        

Real-World Impact

Efficiency: Doctors can quickly receive preliminary diagnoses and treatment suggestions, significantly reducing the time needed for critical decision-making.

Accuracy: AI-powered analysis enhances diagnostic accuracy, minimizing the risk of human error and improving patient outcomes.

Accessibility: This system can support healthcare providers in regions with a shortage of specialists, ensuring patients receive timely and accurate care.

Unlocking New Possibilities in Healthcare

The fusion of cutting-edge technology and medical expertise has the potential to revolutionize healthcare. With Phi-3-Vision, we can move towards a future where AI-driven insights significantly enhance the efficiency and accuracy of medical diagnoses and treatment plans.

Rakesh Maasti, Generative AI Architect & Principal AI Scientist