数字巴林特:在反思实践中使用人工智能
Marcus Lewis1,2, Benedict Hayhoe3
1Abbey Medical Centre, London, UK.
概括
大型语言模型 (LLM) 显示了增强全科医生的反思实践的潜力. 人工智能产生的问题可能有助于全科医生,特别是那些隔离的人,深化自我反思,弥合理论与实践之间的差距.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 专业发展专业发展
背景情况:
- 反思性实践对于研究生全科医生 (GP) 培训和持续专业发展至关重要.
- 时间限制和专业隔离阻碍了全科医生之间对反思性实践的有意义参与.
- 探索创新工具以支持反思性实践对于医疗专业人员来说至关重要.
研究的目的:
- 调查大型语言模型 (LLM) 在增强全科医生的反思性实践方面的潜力.
- 探索LLM产生的问题如何激发更深层次的自我反思,弥合理论与实践之间的差距.
- 评估LLMs对孤立的从业者和实习生的实用性.
主要方法:
- 为了探索LLMs的能力,进行了三项案例研究.
- 使用LLM,为全科医生生成激发思维的问题.
- 该研究考察了LLM能够促使全科医生考虑新的视角和潜在因素的能力.
主要成果:
- 课程展示了在产生激发更深层次自我反思的问题方面的潜力.
- 研究结果表明,LLM可以帮助重塑经验并支持孤立的从业者.
- 人工智能产生的问题可以补充在指导下训练员的传统反思方法.
结论:
- 法学士提供了一个有前途的途径,以提高一般实践中的反思性实践.
- 负责任地整合LLM可以支持孤立的全科医生和实习生,改善专业发展.
- 为了成功实施,必须解决与隐私,过度依赖和偏见有关的伦理考虑.
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