Automated Medical Conference Summarization with AI Integration

Automated Medical Conference Presentation Summarization

1. Data Collection

1.1 Video Acquisition

Collect video recordings of medical conference presentations from various sources.

1.2 Metadata Extraction

Extract relevant metadata (e.g., speaker name, presentation title, date) using AI-driven tools like Google Cloud Video Intelligence.

2. Video Processing

2.1 Speech Recognition

Utilize AI-powered speech-to-text tools such as IBM Watson Speech to Text to convert spoken content into text format.

2.2 Content Segmentation

Implement algorithms to segment the video into meaningful sections based on topics discussed, using tools like Microsoft Azure Video Indexer.

3. Summarization

3.1 Text Summarization

Apply natural language processing (NLP) techniques with tools like OpenAI’s GPT-3 to generate concise summaries of each segmented section.

3.2 Highlight Extraction

Identify key points and highlights from the summarized text using AI algorithms that prioritize critical information.

4. Quality Assurance

4.1 Review Process

Incorporate a manual review stage where healthcare professionals validate the accuracy of the summaries and highlights.

4.2 Feedback Loop

Utilize feedback from reviewers to refine AI models and improve future summarization accuracy.

5. Distribution

5.1 Format Conversion

Convert the summarized content into various formats (e.g., PDF, HTML) for easy access and sharing.

5.2 Dissemination

Share the final summaries with stakeholders through platforms such as Slack or Microsoft Teams, ensuring accessibility for all relevant parties.

6. Continuous Improvement

6.1 Data Analysis

Analyze user engagement and feedback on the summaries to identify areas for improvement.

6.2 Model Retraining

Regularly update and retrain AI models based on new data and insights to enhance summarization effectiveness.