ChatGPT in medical imaging higher education.

INTRODUCTION: Academic integrity among radiographers and nuclear medicine technologists/scientists in both higher education and scientific writing has been challenged by advances in artificial intelligence (AI). The recent release of ChatGPT, a chatbot powered by GPT-3.5 capable of producing accurate and human-like responses to questions in real-time, has redefined the boundaries of academic and scientific writing. These boundaries require objective evaluation. METHOD: ChatGPT was tested against six subjects across the first three years of the medical radiation science undergraduate course for both exams (n = 6) and written assignment tasks (n = 3). ChatGPT submissions were marked against standardised rubrics and results compared to student cohorts. Submissions were also evaluated by Turnitin for similarity and AI scores. RESULTS: ChatGPT powered by GPT-3.5 performed below the average student performance in all written tasks with an increasing disparity as subjects advanced. ChatGPT performed better than the average student in foundation or general subject examinations where shallow responses meet learning outcomes. For discipline specific subjects, ChatGPT lacked the depth, breadth, and currency of insight to provide pass level answers. CONCLUSION: ChatGPT simultaneously poses a risk to academic integrity in writing and assessment while affording a tool for enhanced learning environments. These risks and benefits are likely to be restricted to learning outcomes of lower taxonomies. Both risks and benefits are likely to be constrained by higher order taxonomies. IMPLICATIONS FOR PRACTICE: ChatGPT powered by GPT3.5 has limited capacity to support student cheating, introduces errors and fabricated information, and is readily identified by software as AI generated. Lack of depth of insight and appropriateness for professional communication also limits capacity as a learning enhancement tool.

The impacts of vertical off-centring, tube voltage, and phantom size on computed tomography numbers: An experimental study.

INTRODUCTION: This experimental study explored the effect of vertical off-centring on computed tomography (CT) numbers in combination with various tube voltages and phantom sizes for two CT units. METHODS: CIRS Model 062 Electron Density and system performance phantoms were imaged on Siemens Emotion 16-slice CT and GEMINI-GXL scanners, respectively. Uniformity and accuracy were evaluated as a function of vertical off-centring (20, 40, 60, and 80 mm above the gantry isocentre) using different water phantom sizes (18, 20, and 30 cm) and tube voltages (80, 90, 110, 120, 130 and 140 kVp). RESULTS: Vertical off-centring and phantom size accounted for 92% of the recorded variance and the resultant change in CT numbers. The uniformity test recorded maximum changes of 14 and 27.2 HU for peripheral ROIs across the X- and Y-axes for an 80 mm phantom shift above the gantry isocentre on the GEMINI GXL and Siemens scanners, respectively. The absolute CT number differences between the superior and inferior ROIs were 13.7 HU for the 30 cm phantom and 4.8 HU for the 20 cm phantom for 80 mm vertical off-centring. The largest differences were observed at lower tube voltages. CONCLUSIONS: It is essential to highlight the significance of CT number variation in clinical decision-making. Phantom off-centring affected the uniformity of these numbers, which were further impacted by the ROI position in this experimental study. CT number variation was more evident in peripheral phantom areas, lower tube voltages and larger phantom sizes. IMPLICATIONS FOR PRACTICE: CT number is observed to be a variable under certain common conditions. This significantly impacts several applications where clinical decisions depend on CT number accuracy for tissue lesion characterisation.