PDF(Pubmed)
Abstract：
BACKGROUND: Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One barrier for successful innovation is the scarcity of research in the current literature seeking and analyzing the views of AI or ML researchers and physicians to support ethical guidance.
OBJECTIVE: This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine.
METHODS: Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set.
RESULTS: Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams.
CONCLUSIONS: These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings.
OBJECTIVE: This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine.
METHODS: Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set.
RESULTS: Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams.
CONCLUSIONS: These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings.
摘要：
背景：利用人工智能（AI）和机器学习（ML）的创新工具正在迅速开发用于医学，随着预测中出现的新应用，诊断,以及一系列疾病的治疗，患者群体，和临床程序。成功创新的一个障碍是当前文献中缺乏寻求和分析AI或ML研究人员和医生的观点以支持伦理指导的研究。
目的：本研究旨在描述，使用定性的方法，AI或ML研究人员和专业接触AI或ML工具的医生在AI和ML在医学中的开发和使用中观察或预期的道德问题景观。
方法：使用半结构化访谈来促进深入，开放式讨论,并使用有目的的抽样技术来识别和招募参与者。我们对AI和ML研究人员（n=10）和医生（n=11）的有目的样本进行了21次半结构化访谈。我们询问了受访者对与在医学中采用AI和ML有关的道德考虑的看法。我们的研究小组成员对访谈进行了转录和鉴定。数据分析遵循定性内容分析的原则。这种方法,其中转录的数据被分解为描述性单位，这些单位根据其内容进行命名和排序，允许直接从数据集中归纳出现代码。
结果：值得注意的是，研究人员和医生都表达了对人工智能和机器学习创新在早期发展中如何形成的担忧(即，问题制定阶段)。考虑因素包括评估研究重点和动机，临床需求的清晰度和中心性，研究团队的专业和人口多样性，以及跨学科的知识生成和协作。受访者确定的第一阶段伦理问题在本质上是跨学科的，并邀请了关于如何调整跨学科的优先事项和价值观，并在整个医学AI和ML的开发和实施过程中确保临床价值的问题。相关地，受访者建议跨学科解决这些问题，例如,更多资源来支持开发人员和医生之间的知识生成和协作，与更广泛的利益相关者接触，并努力在广泛的研究和个人团队内部增加研究的多样性。
结论：这些定性发现有助于阐明AI和ML在医疗保健方面预期或遇到的一些伦理挑战。我们的研究是独一无二的，因为它使用开放式问题允许受访者探索他们的情绪和观点，而不会过度依赖关于AI和ML目前是什么或不是什么的隐含假设。这个分析,然而,不包括其他相关利益相关者团体的观点，如患者，伦理学家,行业研究人员或代表，或医生以外的其他医疗保健专业人员。需要额外的定性和定量研究来重现和建立这些发现。
目的：本研究旨在描述，使用定性的方法，AI或ML研究人员和专业接触AI或ML工具的医生在AI和ML在医学中的开发和使用中观察或预期的道德问题景观。
方法：使用半结构化访谈来促进深入，开放式讨论,并使用有目的的抽样技术来识别和招募参与者。我们对AI和ML研究人员（n=10）和医生（n=11）的有目的样本进行了21次半结构化访谈。我们询问了受访者对与在医学中采用AI和ML有关的道德考虑的看法。我们的研究小组成员对访谈进行了转录和鉴定。数据分析遵循定性内容分析的原则。这种方法,其中转录的数据被分解为描述性单位，这些单位根据其内容进行命名和排序，允许直接从数据集中归纳出现代码。
结果：值得注意的是，研究人员和医生都表达了对人工智能和机器学习创新在早期发展中如何形成的担忧(即，问题制定阶段)。考虑因素包括评估研究重点和动机，临床需求的清晰度和中心性，研究团队的专业和人口多样性，以及跨学科的知识生成和协作。受访者确定的第一阶段伦理问题在本质上是跨学科的，并邀请了关于如何调整跨学科的优先事项和价值观，并在整个医学AI和ML的开发和实施过程中确保临床价值的问题。相关地，受访者建议跨学科解决这些问题，例如,更多资源来支持开发人员和医生之间的知识生成和协作，与更广泛的利益相关者接触，并努力在广泛的研究和个人团队内部增加研究的多样性。
结论：这些定性发现有助于阐明AI和ML在医疗保健方面预期或遇到的一些伦理挑战。我们的研究是独一无二的，因为它使用开放式问题允许受访者探索他们的情绪和观点，而不会过度依赖关于AI和ML目前是什么或不是什么的隐含假设。这个分析,然而,不包括其他相关利益相关者团体的观点，如患者，伦理学家,行业研究人员或代表，或医生以外的其他医疗保健专业人员。需要额外的定性和定量研究来重现和建立这些发现。
