Department of Business & Information Systems Engineering, University of Bayreuth, Bayreuth, Germany.
Centre for Future Enterprise, Queensland University of Technology, Brisbane, Australia.
J Med Internet Res. 2022 Jan 27;24(1):e28916. doi: 10.2196/28916.
General practitioners (GPs) care for a large number of patients with various diseases in very short timeframes under high uncertainty. Thus, systems enabled by artificial intelligence (AI) are promising and time-saving solutions that may increase the quality of care.
This study aims to understand GPs' attitudes toward AI-enabled systems in medical diagnosis.
We interviewed 18 GPs from Germany between March 2020 and May 2020 to identify determinants of GPs' attitudes toward AI-based systems in diagnosis. By analyzing the interview transcripts, we identified 307 open codes, which we then further structured to derive relevant attitude determinants.
We merged the open codes into 21 concepts and finally into five categories: concerns, expectations, environmental influences, individual characteristics, and minimum requirements of AI-enabled systems. Concerns included all doubts and fears of the participants regarding AI-enabled systems. Expectations reflected GPs' thoughts and beliefs about expected benefits and limitations of AI-enabled systems in terms of GP care. Environmental influences included influences resulting from an evolving working environment, key stakeholders' perspectives and opinions, the available information technology hardware and software resources, and the media environment. Individual characteristics were determinants that describe a physician as a person, including character traits, demographic characteristics, and knowledge. In addition, the interviews also revealed the minimum requirements of AI-enabled systems, which were preconditions that must be met for GPs to contemplate using AI-enabled systems. Moreover, we identified relationships among these categories, which we conflate in our proposed model.
This study provides a thorough understanding of the perspective of future users of AI-enabled systems in primary care and lays the foundation for successful market penetration. We contribute to the research stream of analyzing and designing AI-enabled systems and the literature on attitudes toward technology and practice by fostering the understanding of GPs and their attitudes toward such systems. Our findings provide relevant information to technology developers, policymakers, and stakeholder institutions of GP care.
全科医生（GP）在非常短的时间内照顾大量患有各种疾病的患者，并且面临高度的不确定性。因此，人工智能（AI）支持的系统是有前途且节省时间的解决方案，可能会提高医疗质量。
本研究旨在了解全科医生对医疗诊断中 AI 支持系统的态度。
我们于 2020 年 3 月至 5 月间采访了 18 名德国全科医生，以确定全科医生对基于 AI 的诊断系统的态度的决定因素。通过分析访谈记录，我们确定了 307 个开放代码，然后进一步对其进行结构化，以得出相关的态度决定因素。
我们将开放代码合并为 21 个概念，最终合并为五个类别：担忧、期望、环境影响、个体特征和 AI 支持系统的最低要求。担忧包括参与者对 AI 支持系统的所有疑虑和恐惧。期望反映了全科医生对 AI 支持系统在全科医疗方面的预期收益和局限性的想法和信念。环境影响包括由不断发展的工作环境、主要利益相关者的观点和意见、可用的信息技术硬件和软件资源以及媒体环境产生的影响。个体特征是描述医生作为个体的决定因素，包括性格特征、人口统计学特征和知识。此外，访谈还揭示了 AI 支持系统的最低要求，这些要求是全科医生考虑使用 AI 支持系统的前提条件。此外，我们还确定了这些类别的关系，并在我们提出的模型中对其进行了合并。
本研究全面了解了初级保健中 AI 支持系统未来用户的观点，并为成功的市场渗透奠定了基础。我们通过促进对全科医生及其对这些系统的态度的理解，为分析和设计 AI 支持系统的研究以及技术和实践态度的文献做出了贡献。我们的研究结果为技术开发者、政策制定者和全科医生护理的利益相关机构提供了相关信息。
