An intelligent assistant for physicians.

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Clinical Decision Support Systems

Background:

• Physician examination processes can be time-consuming and prone to errors.
• Current electronic medical records systems offer limited support beyond basic data management.
• There is a need for intelligent tools to enhance diagnostic accuracy and efficiency.

Purpose of the Study:

• To present a novel software tool designed to assist physicians during patient examinations.
• To transition from traditional electronic medical records to an intelligent physician assistant.
• To improve the speed and accuracy of the examination reporting process.

Main Methods:

• Development of a modular software tool with an intelligent component.
• Implementation of multilabel learning techniques for the intelligent module.
• Integration of user inputs and established medical standards for generating suggestions.

Main Results:

• Preliminary results demonstrate high performance of the intelligent module in a real-world test case.
• The system shows effectiveness even with a limited amount of training data.
• The tool successfully extracts new knowledge and improves with physician usage.

Conclusions:

• The developed intelligent software tool can significantly enhance physician examination processes.
• Multilabel learning is a viable approach for creating effective clinical decision support systems.
• The tool's ability to learn and adapt suggests potential for widespread adoption in healthcare settings.