JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) tools have the potential to enhance personalized clinical care, particularly in radiology. However, their integration into clinical workflows remains complex, especially in pediatric oncology, where early cancer detection is critical. Children with Li-Fraumeni Syndrome (LFS), a rare cancer predisposition disorder, undergo regular surveillance whole-body MRI (wbMRI), which presents an opportunity for AI-assisted tumour detection.

Objective:

We aim to evaluate the impact of an AI-based tumour prediction tool we developed on radiologist performance during wbMRI interpretation in pediatric patients with LFS.

Methods:

We developed a patch-based AI segmentation model trained on augmented 2D slices from 675 surveillance wbMRI volumes of pediatric patients with LFS. The model was designed to highlight regions with high tumour probability. A reader study was conducted with two radiologists who independently reviewed wbMRI cases both with and without AI assistance. We measured evaluation time, number and location of tumours identified, type of follow-up, and subjective feedback using structured questionnaires.

Results:

AI assistance altered interpretation workflows for both radiologists, with mixed effects. On average, the time required to evaluate each case increased when using the AI tool for both radiologists. However, one radiologist had an increase in the number of points selected with the tool, and one had a decrease in the number of points selected with the tool. Subjective feedback indicated that one of the radiologists found a greater difference in their perception between performing with the tool versus without the AI tool; however, both radiologists felt that the task was less difficult and less stressful with the AI tool. Inter-rater variability was evident, underscoring the need for personalized calibration of AI tools.

Conclusions:

AI-assisted wbMRI interpretation can improve tumour detection in pediatric cancer surveillance by reducing false negatives. However, its influence on workflow efficiency and inter-radiologist variability highlights the importance of careful implementation. Successful integration requires addressing challenges such as improving the predictive precision of AI models, offering intuitive end-user designs and instructions, and building trust in AI outputs. This can help ensure better patient outcomes in addition to reduced clinician burnout.