为生物学和医学开发ChatGPT:对生物医学问题和答案的完整审查
1Department of Computer Science and Engineering, the Chinese University of Hong Kong, Hong Kong 999077, China.
Biophysics reports
|July 19, 2024
概括
大型语言模型和多式人工智能正在通过整合各种数据进行诊断和治疗建议来推进医疗问题答案 (MDQA). 这篇评论指导研究人员选择最佳的AI机制用于医疗应用.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理 (NLP) 是一种自然语言处理.
- 多式联络AI 多式联络AI
背景情况:
- 目前的人工智能模型越来越多地包含医疗领域的数据.
- 弥合人类语言和复杂的医学知识之间的差距至关重要.
- 处理多样化,大规模和未标记的医疗数据存在挑战.
研究的目的:
- 为人工智能驱动的医学问答 (MDQA) 提供战略蓝图.
- 引导研究界选择适合医疗研究的人工智能机制.
- 审查医疗保健支持的语言模型和多式联络模式的进展.
主要方法:
- 使用自然语言处理 (NLP) 和多式人工智能范式.
- 将一般领域的数据 (文本,图像,视频) 转移到医疗领域.
- 分析专业任务:单模QA,阅读理解,推理,诊断,关系提取,概率建模和多模QA,视觉QA,图像标题,交叉模式检索,报告总结和生成.
主要成果:
- 医疗领域问题解答 (MDQA) 的加速进展.
- 在处理多样化和复杂的医疗数据场景方面表现出有效性.
- 详细讨论医疗人工智能的结构和进步与一般领域方法相比.
结论:
- 人工智能,特别是语言模型和多式联络方法,在医疗保健支持方面显示出巨大的潜力.
- 该审查强调了医疗AI研究当前的挑战和未来的机遇.
- 这项工作为医疗问题答案的未来创新和应用提供了路线图.
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