The ability to recognize the presence of pathology is a valuable skill for the MR technologist. While it is not within the scope of practice of technologists to read images, recognizing pathology allows the technologist to alert the radiologist while the patient is still in the MR suite. Protocols can then be modified during the study rather than recalling the patient for additional imaging. As a result, throughput and patient satisfaction are enhanced by tailoring studies to the needs of the patient. A recognition and understanding of pathology also provides the technologist with a better understanding of the objective of the study,
a tool to optimize image quality to properly identify pathology, and an enhanced sense of contributing to the delivery of better healthcare to the patient. This insight will motivate efforts to improve the quality of the study.
MR Pathology for the Technologist
Sep 7th, 2009Sep 9th, 2009
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![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)




