{"title":"病人和医生对人工智能的焦虑。","authors":"Wenyu Li, Xueen Liu","doi":"10.1016/j.pec.2024.108619","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This paper investigates the anxiety surrounding the integration of artificial intelligence (AI) in doctor-patient interactions, analyzing the perspectives of both patients and healthcare providers to identify key concerns and potential solutions.</p><p><strong>Methods: </strong>The study employs a comprehensive literature review, examining existing research on AI in healthcare, and synthesizes findings from various surveys and studies that explore the attitudes of patients and doctors towards AI applications in medical settings.</p><p><strong>Results: </strong>The analysis reveals that patient anxiety encompasses algorithm aversion, robophobia, lack of humanistic care, challenges in human-machine interaction, and concerns about AI's universal applicability. Doctors' anxieties stem from fears of replacement, legal liabilities, emotional impacts of work environment changes, and technological apprehension. The paper highlights the need for patient participation, humanistic care, improved interaction methods, educational training, and policy guidelines to foster public understanding and trust in AI.</p><p><strong>Conclusion: </strong>The paper concludes that addressing AI anxiety in doctor-patient relationships is crucial for successfully integrating AI in healthcare. It emphasizes the importance of respecting patient autonomy, addressing the lack of humanistic care, and improving patient-AI interaction to enhance the patient experience and reduce medical errors.</p><p><strong>Practice implications: </strong>The study suggests that future research should focus on understanding the needs and concerns of patients and doctors, strengthening medical humanities education, and establishing policies to guide the ethical use of AI in medicine. It also recommends public education to enhance understanding and trust in AI to improve medical services and ensure professional development and stable work environment for doctors.</p>","PeriodicalId":49714,"journal":{"name":"Patient Education and Counseling","volume":"133 ","pages":"108619"},"PeriodicalIF":2.9000,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Anxiety about artificial intelligence from patient and doctor-physician.\",\"authors\":\"Wenyu Li, Xueen Liu\",\"doi\":\"10.1016/j.pec.2024.108619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This paper investigates the anxiety surrounding the integration of artificial intelligence (AI) in doctor-patient interactions, analyzing the perspectives of both patients and healthcare providers to identify key concerns and potential solutions.</p><p><strong>Methods: </strong>The study employs a comprehensive literature review, examining existing research on AI in healthcare, and synthesizes findings from various surveys and studies that explore the attitudes of patients and doctors towards AI applications in medical settings.</p><p><strong>Results: </strong>The analysis reveals that patient anxiety encompasses algorithm aversion, robophobia, lack of humanistic care, challenges in human-machine interaction, and concerns about AI's universal applicability. Doctors' anxieties stem from fears of replacement, legal liabilities, emotional impacts of work environment changes, and technological apprehension. The paper highlights the need for patient participation, humanistic care, improved interaction methods, educational training, and policy guidelines to foster public understanding and trust in AI.</p><p><strong>Conclusion: </strong>The paper concludes that addressing AI anxiety in doctor-patient relationships is crucial for successfully integrating AI in healthcare. It emphasizes the importance of respecting patient autonomy, addressing the lack of humanistic care, and improving patient-AI interaction to enhance the patient experience and reduce medical errors.</p><p><strong>Practice implications: </strong>The study suggests that future research should focus on understanding the needs and concerns of patients and doctors, strengthening medical humanities education, and establishing policies to guide the ethical use of AI in medicine. It also recommends public education to enhance understanding and trust in AI to improve medical services and ensure professional development and stable work environment for doctors.</p>\",\"PeriodicalId\":49714,\"journal\":{\"name\":\"Patient Education and Counseling\",\"volume\":\"133 \",\"pages\":\"108619\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Patient Education and Counseling\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.pec.2024.108619\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Patient Education and Counseling","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.pec.2024.108619","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Anxiety about artificial intelligence from patient and doctor-physician.
Objective: This paper investigates the anxiety surrounding the integration of artificial intelligence (AI) in doctor-patient interactions, analyzing the perspectives of both patients and healthcare providers to identify key concerns and potential solutions.
Methods: The study employs a comprehensive literature review, examining existing research on AI in healthcare, and synthesizes findings from various surveys and studies that explore the attitudes of patients and doctors towards AI applications in medical settings.
Results: The analysis reveals that patient anxiety encompasses algorithm aversion, robophobia, lack of humanistic care, challenges in human-machine interaction, and concerns about AI's universal applicability. Doctors' anxieties stem from fears of replacement, legal liabilities, emotional impacts of work environment changes, and technological apprehension. The paper highlights the need for patient participation, humanistic care, improved interaction methods, educational training, and policy guidelines to foster public understanding and trust in AI.
Conclusion: The paper concludes that addressing AI anxiety in doctor-patient relationships is crucial for successfully integrating AI in healthcare. It emphasizes the importance of respecting patient autonomy, addressing the lack of humanistic care, and improving patient-AI interaction to enhance the patient experience and reduce medical errors.
Practice implications: The study suggests that future research should focus on understanding the needs and concerns of patients and doctors, strengthening medical humanities education, and establishing policies to guide the ethical use of AI in medicine. It also recommends public education to enhance understanding and trust in AI to improve medical services and ensure professional development and stable work environment for doctors.
期刊介绍:
Patient Education and Counseling is an interdisciplinary, international journal for patient education and health promotion researchers, managers and clinicians. The journal seeks to explore and elucidate the educational, counseling and communication models in health care. Its aim is to provide a forum for fundamental as well as applied research, and to promote the study of organizational issues involved with the delivery of patient education, counseling, health promotion services and training models in improving communication between providers and patients.