病理学和医学中的生成人工智能:更深入的潜水
Hooman H Rashidi1, Joshua Pantanowitz2, Alireza Chamanzar1
1Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
概括
生成型人工智能 (Gen AI) 在病理学和医学方面提供了变革性的潜力,在诊断,培训和教育方面有应用. 本综述探讨了Gen AI模型,工具和未来的医疗保健影响,包括好处和挑战.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 计算病理学计算病理学
背景情况:
- 生成型人工智能 (Gen AI) 正在快速发展,为医疗实践的整合提供了重大机会.
- 病理学和医学正在探索新的AI应用程序,以提高诊断准确性,研究和教育.
研究的目的:
- 为病理学和医学中生成人工智能应用提供全面的审查.
- 探索医疗领域内的各种Gen AI模型,框架及其局限性.
- 讨论一代AI在医疗保健中的未来影响,好处和挑战.
主要方法:
- 审查当前文献和流行的Gen AI模型 (GPT-4,Llama,DALL-E,稳定扩散).
- 探索一代人工智能应用:聊天机器人,合成图像生成,数据增强和教育工具.
- 对Gen AI框架 (变压器,GAN,扩散模型) 和必要的库/工具的分析.
- 讨论伦理,隐私,安全和成本方面的考虑.
主要成果:
- 一代人工智能模型展示了各种应用,包括诊断报告生成,合成数据创建和增强医学教育.
- 已经确定了流行的Gen AI模型和框架,以及它们在医疗环境中的特定局限性.
- 已经审查了在医疗保健中实施Gen AI的基本工具和库.
结论:
- 人工智能将对病理学和医学产生革命性影响,提供改进的诊断和个性化的患者护理.
- 解决与隐私,偏见,伦理和安全相关的挑战对于成功将Gen AI整合到医疗保健中至关重要.
- 医疗保健的未来可能涉及复杂的Gen AI工具,需要谨慎的开发和部署策略.
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