放射学中的ChatGPT:对性能,陷和未来前景的系统审查
Pedram Keshavarz1, Sara Bagherieh2, Seyed Ali Nabipoorashrafi3
1Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; School of Science and Technology, The University of Georgia, Tbilisi 0171, Georgia.
在84.1%的放射学研究中,ChatGPT显示出高性能,但偏见和错误信息等局限性需要进一步研究. 需要更多的多中心研究来确认其在临床放射学中的作用.
科学领域:
- 医学中的人工智能
- 放射学信息学 放射学信息学
- 自然语言处理应用程序
背景情况:
- 像ChatGPT这样的大型语言模型越来越多地被用于临床应用.
- 放射学为人工智能集成提供了独特的机会和挑战.
研究的目的:
- 在放射学中系统地审查ChatGPT的性能.
- 确定局限性和伦理考虑.
- 探索未来的整合和优化策略.
主要方法:
- 在PubMed,科学网,Embase和谷歌学者中进行全面的文献搜索.
- 包括到2024年1月1日发表的评估临床放射学中ChatGPT的研究.
- 分析了绩效指标,局限性和道德问题.
主要成果:
- 44项研究评估了ChatGPT;84.1%的研究显示出高性能,特别是在诊断和决策支持方面.
- 准确度中位数为70.5%,与参考标准一致率为83.6%.
- 聊天GPT-4的表现优于GPT-3.5,表现出更好的理解和准确性.
- 确定的风险包括偏见,错误信息,隐私问题和网络安全漏洞.
结论:
- 聊天GPT在放射学中显示出显著的潜力,报告的有效性很高.
- 准确性,偏见和道德问题等局限性需要进一步调查.
- 用各种数据集进行广泛的多中心研究对于验证ChatGPT在放射学中的作用至关重要.
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