The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.
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Quantum机器学习目前正在受到极大的关注，但是与实用应用的经典机器学习技术相比，其有用性尚不清楚。但是，有迹象表明，某些量子机学习算法可能会提高其经典同行的培训能力 - 在很少有培训数据的情况下，这在情况下可能特别有益。这种情况自然出现在医学分类任务中。在本文中，提出了不同的杂种量子卷积神经网络（QCCNN），提出了不同的量子电路设计和编码技术。它们应用于二维医学成像数据，例如在计算机断层扫描中具有不同的，潜在的恶性病变。这些QCCNN的性能已经与它们的经典同行之一相似，因此鼓励进一步研究将这些算法应用于医学成像任务的方向。
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