Dr. Anton Becker, PhD, and David Gugelmann, PhD

AI
AI-cyberterrorism in radiology: Threat or red herring?
In this Second Opinion article, Dr. Anton Becker, PhD, of Memorial Sloan Cancer Kettering Cancer in New York City and David Gugelmann, PhD, of Exeon Analytics assess the threat posed by artificial intelligence-powered cyberterrorism in radiology.
December 11, 2019
2019 03 26 18 32 2300 Computer Hacker Security 400
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DEXA reveals tea consumption supports bone health
Moderate-frequency tea consumption was associated with better bone mineral status and more favorable longitudinal changes.
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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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Model confidence, reader expertise affect reader-LLM collaboration
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