UNLABELLED: This study evaluated lesion mislocalization between PET and CT on PET/CT studies when CT instead of germanium is used for attenuation correction (AC). METHODS: PET/CT scans were obtained for 300 clinical patients. Both CT and germanium scans were used to correct PET emission data. Cases were noted of suspected inaccurate localization of lesions on any of the 5 sets of images (PET using germanium AC [GeAC] fused and not fused with CT, PET using CT AC fused and not fused with CT, and PET with no AC [NAC]). Independent CT or MRI was used to determine true lesion locations. RESULTS: Six of 300 patients (2%) had lesion mislocalization when CT was used for AC or fusion. True liver dome lesions were mislocalized to the right lung base on PET/CT, likely because of a respiratory motion difference between PET and CT. No mislocalization was present on NAC PET or non-CT-fused GeAC PET images. CONCLUSION: Serious lesion mislocalization on PET/CT studies may occur, albeit very infrequently, when CT is used for either AC or fusion.
Background The availability of large language models such as Chat Generative Pre-trained Transformer (ChatGPT, OpenAI) has enabled individuals from diverse backgrounds to access medical information. However, concerns exist about the accuracy of ChatGPT responses and the references used to generate medical content. Methods This observational study investigated the authenticity and accuracy of references in medical articles generated by ChatGPT. ChatGPT-3.5 generated 30 short medical papers, each with at least three references, based on standardized prompts encompassing various topics and therapeutic areas. Reference authenticity and accuracy were verified by searching Medline, Google Scholar, and the Directory of Open Access Journals. The authenticity and accuracy of individual ChatGPT-generated reference elements were also determined. Results Overall, 115 references were generated by ChatGPT, with a mean of 3.8±1.1 per paper. Among these references, 47% were fabricated, 46% were authentic but inaccurate, and only 7% were authentic and accurate. The likelihood of fabricated references significantly differed based on prompt variations; yet the frequency of authentic and accurate references remained low in all cases. Among the seven components evaluated for each reference, an incorrect PMID number was most common, listed in 93% of papers. Incorrect volume (64%), page numbers (64%), and year of publication (60%) were the next most frequent errors. The mean number of inaccurate components was 4.3±2.8 out of seven per reference. Conclusions The findings of this study emphasize the need for caution when seeking medical information on ChatGPT since most of the references provided were found to be fabricated or inaccurate. Individuals are advised to verify medical information from reliable sources and avoid relying solely on artificial intelligence-generated content.
Discrimination has been widely studied in the social sciences. Economists often categorize the source of discrimination as either taste-based or statistical—a valuable distinction for policy design and welfare analysis. In this paper, we highlight that in many situations economic agents may have inaccurate beliefs, and demonstrate that the possibility of inaccurate statistical discrimination generates an identification problem for attempts to isolate the source of differential treatment. We introduce isodiscrimination curves —which represent the set of preferences and beliefs that generate the same level of discrimination—to formally outline the identification problem: when not accounted for, inaccurate statistical discrimination can be mistaken for taste-based discrimination, accurate statistical discrimination, or their combination. A review of the empirical discrimination literature in economics, spanning 1990-2018, reveals the scope of this issue. While most papers discuss and attempt to distinguish between taste and statistical discrimination, a small minority—fewer than 7%—consider inaccurate beliefs in the analysis. An experiment illustrates a methodology for differentiating between the three sources of discrimination, demonstrating the pitfalls of the identification problem while presenting a portable solution.
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