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This prospective exploratory study conducted from January 2023 through May 2023 evaluated the ability of ChatGPT to answer questions from Brazilian radiology board examinations, exploring how different prompt strategies can influence performance using GPT-3.5 and GPT-4. Three multiple-choice board examinations that did not include image-based questions were evaluated: (a) radiology and diagnostic imaging, (b) mammography, and (c) neuroradiology. Five different styles of zero-shot prompting were tested: (a) raw question, (b) brief instruction, (c) long instruction, (d) chain-of-thought, and (e) question-specific automatic prompt generation (QAPG). The QAPG and brief instruction prompt strategies performed best for all examinations (P < .05), obtaining passing scores (≥60%) on the radiology and diagnostic imaging examination when testing both versions of ChatGPT. The QAPG style achieved a score of 60% for the mammography examination using GPT-3.5 and 76% using GPT-4. GPT-4 achieved a score up to 65% in the neuroradiology examination. The long instruction style consistently underperformed, implying that excessive detail might harm performance. GPT-4's scores were less sensitive to prompt style changes. The QAPG prompt style showed a high volume of the "A" option but no statistical difference, suggesting bias was found. GPT-4 passed all three radiology board examinations, and GPT-3.5 passed two of three examinations when using an optimal prompt style. Keywords: ChatGPT, Artificial Intelligence, Board Examinations, Radiology and Diagnostic Imaging, Mammography, Neuroradiology © RSNA, 2023 See also the commentary by Trivedi and Gichoya in this issue.

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OBJECTIVES: Evaluate the performance of a deep learning (DL)-based model for multiple sclerosis (MS) lesion segmentation and compare it to other DL and non-DL algorithms. METHODS: This ambispective, multicenter study assessed the performance of a DL-based model for MS lesion segmentation and compared it to alternative DL- and non-DL-based methods. Models were tested on internal (n = 20) and external (n = 18) datasets from Latin America, and on an external dataset from Europe (n = 49). We also examined robustness by rescanning six patients (n = 6) from our MS clinical cohort. Moreover, we studied inter-human annotator agreement and discussed our findings in light of these results. Performance and robustness were assessed using intraclass correlation coefficient (ICC), Dice coefficient (DC), and coefficient of variation (CV). RESULTS: Inter-human ICC ranged from 0.89 to 0.95, while spatial agreement among annotators showed a median DC of 0.63. Using expert manual segmentations as ground truth, our DL model achieved a median DC of 0.73 on the internal, 0.66 on the external, and 0.70 on the challenge datasets. The performance of our DL model exceeded that of the alternative algorithms on all datasets. In the robustness experiment, our DL model also achieved higher DC (ranging from 0.82 to 0.90) and lower CV (ranging from 0.7 to 7.9%) when compared to the alternative methods. CONCLUSION: Our DL-based model outperformed alternative methods for brain MS lesion segmentation. The model also proved to generalize well on unseen data and has a robust performance and low processing times both on real-world and challenge-based data. CLINICAL RELEVANCE STATEMENT: Our DL-based model demonstrated superior performance in accurately segmenting brain MS lesions compared to alternative methods, indicating its potential for clinical application with improved accuracy, robustness, and efficiency. KEY POINTS: • Automated lesion load quantification in MS patients is valuable; however, more accurate methods are still necessary. • A novel deep learning model outperformed alternative MS lesion segmentation methods on multisite datasets. • Deep learning models are particularly suitable for MS lesion segmentation in clinical scenarios.

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The intimin protein is the major adhesin involved in the intimate adherence of atypical enteropathogenic Escherichia coli (aEPEC) strains to epithelial cells, but little is known about the structures involved in their early colonization process. A previous study demonstrated that the type III secretion system (T3SS) plays an additional role in the adherence of an Escherichia albertii strain. Therefore, we assumed that the T3SS could be related to the adherence efficiency of aEPEC during the first stages of contact with epithelial cells. To test this hypothesis, we examined the adherence of seven aEPEC strains and their eae (intimin) isogenic mutants in the standard HeLa adherence assay and observed that all wild-type strains were adherent while five isogenic eae mutants were not. The two eae mutant strains that remained adherent were then used to generate the eae/escN double mutants (encoding intimin and the T3SS ATPase, respectively) and after the adherence assay, we observed that one strain lost its adherence capacity. This suggested a role for the T3SS in the initial adherence steps of this strain. In addition, we demonstrated that this strain expressed the T3SS at significantly higher levels when compared to the other wild-type strains and that it produced longer translocon-filaments. Our findings reveal that the T3SS-translocon can play an additional role as an adhesin at the beginning of the colonization process of aEPEC.

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The intimin protein is the major adhesin involved in the intimate adherence of atypicalenteropathogenicEscherichia coli(aEPEC) strains to epithelial cells, but little is knownabout the structures involved in their early colonization process. A previous studydemonstrated that the type III secretion system (T3SS) plays an additional role in theadherence of anEscherichia albertiistrain. Therefore, we assumed that the T3SS couldbe related to the adherence efficiency of aEPEC during the first stages of contactwith epithelial cells. To test this hypothesis, we examined the adherence of sevenaEPEC strains and theireae(intimin) isogenic mutants in the standard HeLa adherenceassay and observed that all wild-type strains were adherent while five isogeniceaemutants were not. The twoeaemutant strains that remained adherent were then usedto generate theeae/escNdouble mutants (encoding intimin and the T3SS ATPase,respectively) and after the adherence assay, we observed that one strain lost itsadherence capacity. This suggested a role for the T3SS in the initial adherence stepsof this strain. In addition, we demonstrated that this strain expressed the T3SS atsignificantly higher levels when compared to the other wild-type strains and that itproduced longer translocon-filaments. Our findings reveal that the T3SS-transloconcan play an additional role as an adhesin at the beginning of the colonization processof aEPEC.