In this paper, we critically evaluate the capabilities of the
state-of-the-art multimodal large language model, i.e., GPT-4 with Vision
(GPT-4V), on Visual Question Answering (VQA) task. Our experiments thoroughly
assess GPT-4V's proficiency in answering questions paired with images using
both pathology and radiology datasets from 11 modalities (e.g. Microscopy,
Dermoscopy, X-ray, CT, etc.) and fifteen objects of interests (brain, liver,
lung, etc.). Our datasets encompass a comprehensive range of medical inquiries,
including sixteen distinct question types. Throughout our evaluations, we
devised textual prompts for GPT-4V, directing it to synergize visual and
textual information. The experiments with accuracy score conclude that the
current version of GPT-4V is not recommended for real-world diagnostics due to
its unreliable and suboptimal accuracy in responding to diagnostic medical
questions. In addition, we delineate seven unique facets of GPT-4V's behavior
in medical VQA, highlighting its constraints within this complex arena. The
complete details of our evaluation cases are accessible at
this https URL

我们对最先进的多模态大型语言模型 GPT-4V 在视觉问答任务中的能力进行了关键评估，实验充分评估了 GPT-4V 在使用包括 11 种模态（如显微镜、皮肤镜、X 射线、CT 等）和十五种感兴趣的对象（如脑、肝脏、肺等）的病理学和放射学数据集中回答带有图像的问题的能力。我们的数据集涵盖了广泛的医学问题和十六种不同的问题类型。通过准确度评分的实验结果表明，目前的 GPT-4V 版本在应对诊断性医学问题方面的准确性不可靠且次优。此外，我们详细描述了 GPT-4V 在医学视觉问答中的七个独特特征，突出了其在这个复杂领域中的局限性。我们评估案例的完整细节可在此 https URL 上找到。

医学应用的多模态 ChatGPT：GPT-4V 的实验研究

Multimodal ChatGPT for Medical Applications: an Experimental Study of  GPT-4V

This paper seeks to address the classification of misinformation in news
articles using a Long Short Term Memory Recurrent Neural Network. Articles were
taken from 2018; a year that was filled with reporters writing about President
Donald Trump, Special Counsel Robert Mueller, the Fifa World Cup, and Russia.
The model presented successfully classifies these articles with an accuracy
score of 0.779944. We consider this to be successful because the model was
trained on articles that included languages other than English as well as
incomplete, or fragmented, articles.

本研究使用长短期记忆循环神经网络来对新闻文章中的错误信息进行分类，并取得了 0.779944 的准确率。文章来源于 2018 年，其成功之处在于模型不仅针对英语文章进行训练，还包括了不完整或不连续的文章。