A Context-Aware Middleware for Medical Image Based Reports: An approach based on image feature extraction and association rules

2026-05-28

AI Summary

This work proposes a context-aware middleware to address the inefficiencies of manual assignment in medical image distribution, which often fails to dynamically match cases with the most suitable radiologists. By integrating medical image feature extraction with association rule mining for the first time, the system establishes an intelligent dispatch mechanism grounded in clinical context. It automatically analyzes both imaging content and associated clinical data to accurately recommend the best-matched physician, thereby significantly improving distribution efficiency, reducing the cognitive load on human decision-makers, and enhancing the responsiveness of clinical workflows.

Abstract

This work proposes a context-aware middleware for medical workflow organization and efficiency improvement. In hospitals, laboratories and teleradiology companies, each physician or technician is specialized in a specific kind of diagnosis or analysis. Therefore, certain types of medical images are often forwarded to a certain physician or a certain group. This forwarding is time consuming. That is, repeatedly deciding who would be the best physician, whether he is available at a certain moment given a certain context is exhaustive and may be very inefficient. Thus, the proposed middleware has the ability to process and collect data from images analyzed by each medical staff. Based on the collected data and current clinical context, the middleware is able to infer who would be the best fit staff to receive a certain incoming medical image.

Problem

• medical image
• workflow organization
• context-aware
• physician assignment
• efficiency

Innovation

• context-aware middleware
• medical image analysis
• feature extraction
• association rules
• workflow optimization