Building an intelligent and efficient medical assistant is still a
challenging AI problem. The major limitation comes from the data modality
scarceness, which reduces comprehensive patient perception. This demo paper
presents the GigaPevt, the first multimodal medical assistant that combines the
dialog capabilities of large language models with specialized medical models.
Such an approach shows immediate advantages in dialog quality and metric
performance, with a 1.18\% accuracy improvement in the question-answering task.

通过结合大型语言模型的对话能力和专门的医学模型，本文提出了 GigaPevt 医学助手，解决了构建智能高效的医学助手的困难性问题，并在对话质量和指标表现上取得了显著的优势，问答任务的准确度提高了 1.18%。

GigaPevt: 多模式医疗助理

GigaPevt: Multimodal Medical Assistant

The advanced large language model (LLM) ChatGPT has shown its potential in
different domains and remains unbeaten due to its characteristics compared to
other LLMs. This study aims to evaluate the potential of using a fine-tuned
ChatGPT model as a personal medical assistant in the Arabic language. To do so,
this study uses publicly available online questions and answering datasets in
Arabic language. There are almost 430K questions and answers for 20
disease-specific categories. GPT-3.5-turbo model was fine-tuned with a portion
of this dataset. The performance of this fine-tuned model was evaluated through
automated and human evaluation. The automated evaluations include perplexity,
coherence, similarity, and token count. Native Arabic speakers with medical
knowledge evaluated the generated text by calculating relevance, accuracy,
precision, logic, and originality. The overall result shows that ChatGPT has a
bright future in medical assistance.

该研究评估了在阿拉伯语中使用经过微调的 ChatGPT 模型作为个人医疗助手的潜力，并通过自动评估和人工评估证明它在医疗援助方面具有光明前景。