Radiology Challenges and Changes

Sep 12th, 2014
San Antonio, TX
US
https://store.aheconline.com/index.php?route=product/product&path=59&product_id=1354
This course demonstrates tips, tricks, and innovative new ways to deal with these challenges and more.
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Median report creation and turnaround times dropped to under one minute.
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Shown is a 69-year-old man with a cholangiocarcinoma in the distal common bile duct. F-18 FDG-PET/CT was negative where Ga-68 FAPI-46 PET/CT showed high focal FAPI uptake in the liver hilum and surrounding lymph nodes. a-d: F-18 FDG-PET/CT, e-h: Ga-68 FAPI-46 PET/CT. a, b, e, f: Maximum intensity projections, c, g: axial fusion images, d, h: non-contrast enhanced CT.
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Model confidence, reader expertise affect reader-LLM collaboration
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Successful human-LLM collaboration is associated with model confidence and reader expertise in chest imaging.
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Example cases illustrate the double-edged role of large language model (LLM) rationale quality. (A) A 50-year-old female patient with a palpable anterior chest wall mass (correct diagnosis: solitary fibrous tumor of the pleura). The LLM incorrectly suggested synovial sarcoma but provided a persuasive rationale (quality score: 4 of 5), describing a well-defined nodule at axial contrast-enhanced CT spanning the pleura and intercostal space with heterogeneous strong enhancement. Four of five readers (80%) accepted the incorrect suggestion. FDG = fluorodeoxyglucose. (B) A 61-year-old male patient with diabetes presented with cough, sputum, and dyspnea (correct diagnosis: mucormycosis). The LLM correctly suggested mucormycosis with an excellent rationale (quality score: 5 of 5), integrating the reverse halo sign on serial axial CT images and rapidly progressive cavitary lesions. Three of five readers initially selected incorrect diagnoses; after LLM output, all five selected the correct diagnosis (100% acceptance). Blue and orange reader icons denote high- and low-expertise readers, respectively.
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