Laurie Lafleur

IS
How IT innovation can improve healthcare in Canada
While Canadians fiercely defend their country's healthcare system and its universal access to care, the Canadian model suffers from woes such as long wait times and a shortage of physicians. Laurie Lafleur of Paragon Consulting Partners suggests that innovation in healthcare IT can help address some of these shortcomings.
May 27, 2018
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AI
Are you ready for a radiologist shortage?
Could the U.S. be heading toward a shortage of radiologists? The idea might not be as crazy as it sounds if current demographic trends in radiology continue. Laurie Lafleur of Paragon Consulting Partners ponders this question and proposes that technologies such as artificial intelligence and blockchain could come to the rescue.
April 2, 2018
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AI system cuts DEXA turnaround time, improves reports
By Will Morton
Median report creation and turnaround times dropped to under one minute.
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Findings support PAE as an important minimally invasive treatment option for elderly patients who are nonsurgical candidates.
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Prone positioning may lead to lower heart and lung doses and lower rates of acute dermatitis.
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Women's Imaging MinnieCast, Episode 15: BACs and heart disease risk
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FAPI-PET/CT shows promise in hepatopancreatobiliary cancers
By Will Morton
FAPI-PET/CT could potentially improve noninvasive cancer staging, and, ultimately, clinical decision making.
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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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By Amerigo Allegretto
A longitudinal DBT model outperformed other models based on mammography and clinical factors in predicting five-year cancer risk.
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Moderate-frequency tea consumption was associated with better bone mineral status and more favorable longitudinal changes.
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AI tools may not meet breast radiologists' expectations in practice
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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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