PDF(Pubmed)
Abstract：
UNASSIGNED: The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education.
UNASSIGNED: This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students.
UNASSIGNED: The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software.
UNASSIGNED: Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders\' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure.
UNASSIGNED: The analysis emphasizes integrating AI into medical curricula to ensure students\' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.
UNASSIGNED: This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students.
UNASSIGNED: The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software.
UNASSIGNED: Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders\' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure.
UNASSIGNED: The analysis emphasizes integrating AI into medical curricula to ensure students\' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.
摘要：
■人工智能（AI）在医疗保健中的重要性日益增加，要求医疗保健专业人员对AI技术有全面的了解，需要适应医学教育。
■本文探讨了利益相关者对人工智能在医学中的看法和期望，并探讨了它们对医学课程的潜在影响。该研究项目旨在评估不同利益相关者的AI经验和意识，并确定医学教育中与AI相关的基本主题，以定义学生的必要能力。
■经验数据是在2022年8月至2023年3月之间作为TüKITZMed项目的一部分收集的，使用半结构化的定性访谈。这些访谈是针对不同的利益相关者进行的，以探索他们在医学中的经验和观点。使用MAXQDA软件对收集的数据进行定性内容分析。
■对38名参与者进行了半结构化访谈(6名讲师，9名临床医生，10名学生,6位AI专家,和7个机构利益相关者)。定性内容分析揭示了6个主要类别，总共24个子类别来回答研究问题。对利益相关者陈述的评估揭示了他们对人工智能理解的几个共同点和不同之处。根据主要类别确定的关键人工智能主题如下：可能的课程内容，技能,和能力；编程技能；课程范围；和课程结构。
■该分析强调将AI整合到医学课程中，以确保学生对临床应用的熟练程度。标准化的AI理解对于定义和教授相关内容至关重要。考虑到实施中的不同观点对于在医疗背景下全面定义人工智能至关重要，弥补差距，为未来人工智能在医学研究中的使用提供有效的解决方案。结果提供了对潜在课程内容和结构的见解，包括人工智能在医学中的各个方面。
■本文探讨了利益相关者对人工智能在医学中的看法和期望，并探讨了它们对医学课程的潜在影响。该研究项目旨在评估不同利益相关者的AI经验和意识，并确定医学教育中与AI相关的基本主题，以定义学生的必要能力。
■经验数据是在2022年8月至2023年3月之间作为TüKITZMed项目的一部分收集的，使用半结构化的定性访谈。这些访谈是针对不同的利益相关者进行的，以探索他们在医学中的经验和观点。使用MAXQDA软件对收集的数据进行定性内容分析。
■对38名参与者进行了半结构化访谈(6名讲师，9名临床医生，10名学生,6位AI专家,和7个机构利益相关者)。定性内容分析揭示了6个主要类别，总共24个子类别来回答研究问题。对利益相关者陈述的评估揭示了他们对人工智能理解的几个共同点和不同之处。根据主要类别确定的关键人工智能主题如下：可能的课程内容，技能,和能力；编程技能；课程范围；和课程结构。
■该分析强调将AI整合到医学课程中，以确保学生对临床应用的熟练程度。标准化的AI理解对于定义和教授相关内容至关重要。考虑到实施中的不同观点对于在医疗背景下全面定义人工智能至关重要，弥补差距，为未来人工智能在医学研究中的使用提供有效的解决方案。结果提供了对潜在课程内容和结构的见解，包括人工智能在医学中的各个方面。
