不孕症患者人工受孕辅助生殖技术与面对面咨询的结果比较:一项横断面研究。

IF 3.6 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL Postgraduate Medical Journal Pub Date : 2024-07-07 DOI:10.1093/postmj/qgae083
Shaolong Cheng, Yuping Xiao, Ling Liu, Xingyu Sun
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引用次数: 0

摘要

背景:随着人工智能(AI)在医疗保健领域的应用,像 ChatGPT 这样的数字平台为传统医疗咨询提供了创新的替代方案。本研究旨在了解人工智能辅助的 ChatGPT 咨询与不孕症患者传统的面对面交流的比较结果:这项横断面研究涉及 120 名不孕不育患者,通过 ChatGPT 和传统面对面方式咨询的患者各占一半。评估的主要结果包括患者满意度、理解力和咨询持续时间。次要结果包括人口统计学信息、临床病史和咨询后的后续行动:结果:两种咨询方法的中位年龄都是 34 岁,但使用 ChatGPT 的患者的满意度(中位数为 4 分,满分为 5 分)明显高于面对面咨询的患者(中位数为 3 分,满分为 5 分;P 结论:使用 ChatGPT 的患者满意度明显高于面对面咨询的患者(中位数为 4 分,满分为 5 分):人工智能辅助 ChatGPT 咨询为辅助生殖医学中传统的面对面咨询提供了一种很有前景的替代方案。虽然使用 ChatGPT 患者满意度更高,会诊时间更短,但仍需进一步研究,以了解人工智能驱动的医疗会诊的长期影响和临床结果。关键信息 本主题的已知信息: 人工智能(AI)应用(如 ChatGPT)已在各种医疗环境中显示出潜力,包括初级保健和心理健康支持。不孕不育是一个重要的全球性健康问题,需要大量的咨询,往往面临着等待时间长和患者满意度参差不齐等挑战。以往的研究表明,人工智能可以提供个性化护理和即时反馈,但与生殖医学领域的传统咨询相比,人工智能的疗效尚未得到充分研究。本研究的补充: 本研究表明,与传统的面对面咨询相比,人工智能辅助的 ChatGPT 咨询可显著提高不孕不育患者的满意度并缩短咨询时间。两种咨询方法在患者理解、人口分布和咨询后的后续行动方面具有可比性。本研究可能对研究、实践或政策产生的影响: 研究结果表明,人工智能驱动的咨询可作为传统方法的一种有效且高效的替代方法,有可能缩短生殖医学领域的咨询时间并提高患者满意度。进一步的研究可以探索人工智能在临床环境中的长期影响和更广泛应用,从而影响未来的医疗实践和政策,促进人工智能技术的整合。
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Comparative outcomes of AI-assisted ChatGPT and face-to-face consultations in infertility patients: a cross-sectional study.

Background: With the advent of artificial intelligence (AI) in healthcare, digital platforms like ChatGPT offer innovative alternatives to traditional medical consultations. This study seeks to understand the comparative outcomes of AI-assisted ChatGPT consultations and conventional face-to-face interactions among infertility patients.

Methods: A cross-sectional study was conducted involving 120 infertility patients, split evenly between those consulting via ChatGPT and traditional face-to-face methods. The primary outcomes assessed were patient satisfaction, understanding, and consultation duration. Secondary outcomes included demographic information, clinical history, and subsequent actions post-consultation.

Results: While both consultation methods had a median age of 34 years, patients using ChatGPT reported significantly higher satisfaction levels (median 4 out of 5) compared to face-to-face consultations (median 3 out of 5; p < 0.001). The ChatGPT group also experienced shorter consultation durations, with a median difference of 12.5 minutes (p < 0.001). However, understanding, demographic distributions, and subsequent actions post-consultation were comparable between the two groups.

Conclusions: AI-assisted ChatGPT consultations offer a promising alternative to traditional face-to-face consultations in assisted reproductive medicine. While patient satisfaction was higher and consultation durations were shorter with ChatGPT, further studies are required to understand the long-term implications and clinical outcomes associated with AI-driven medical consultations. Key Messages What is already known on this topic:  Artificial intelligence (AI) applications, such as ChatGPT, have shown potential in various healthcare settings, including primary care and mental health support. Infertility is a significant global health issue that requires extensive consultations, often facing challenges such as long waiting times and varied patient satisfaction. Previous studies suggest that AI can offer personalized care and immediate feedback, but its efficacy compared with traditional consultations in reproductive medicine was not well-studied. What this study adds:  This study demonstrates that AI-assisted ChatGPT consultations result in significantly higher patient satisfaction and shorter consultation durations compared with traditional face-to-face consultations among infertility patients. Both consultation methods were comparable in terms of patient understanding, demographic distributions, and subsequent actions postconsultation. How this study might affect research, practice, or policy:  The findings suggest that AI-driven consultations could serve as an effective and efficient alternative to traditional methods, potentially reducing consultation times and improving patient satisfaction in reproductive medicine. Further research could explore the long-term impacts and broader applications of AI in clinical settings, influencing future healthcare practices and policies toward integrating AI technologies.

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来源期刊
Postgraduate Medical Journal
Postgraduate Medical Journal 医学-医学:内科
CiteScore
8.50
自引率
2.00%
发文量
131
审稿时长
2.5 months
期刊介绍: Postgraduate Medical Journal is a peer reviewed journal published on behalf of the Fellowship of Postgraduate Medicine. The journal aims to support junior doctors and their teachers and contribute to the continuing professional development of all doctors by publishing papers on a wide range of topics relevant to the practicing clinician and teacher. Papers published in PMJ include those that focus on core competencies; that describe current practice and new developments in all branches of medicine; that describe relevance and impact of translational research on clinical practice; that provide background relevant to examinations; and papers on medical education and medical education research. PMJ supports CPD by providing the opportunity for doctors to publish many types of articles including original clinical research; reviews; quality improvement reports; editorials, and correspondence on clinical matters.
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