In primary healthcare institutions, due to insufficient staffing of radiologists, it is impossible to ensure that they are on duty 24/7, and general practitioners have limited interpretation ability of chest images, which affects the quality and efficiency of medical services.

To explore the application effect of artificial intelligence assisted diagnosis system for chest diseases in primary healthcare institutions, with a focus on evaluating its role in improving the diagnostic efficiency, accuracy, and patient satisfaction of general practitioners.

During the vacation period of radiologists in April 2024, 16 general practitioners from Donghua Community Center were selected as the research subjects. They were randomly divided into a general practitioner+AI group of 8 and a control group of 8. At the same time, a total of 100 respiratory system disease patients who required X-ray imaging examination were included in the two groups of doctors, with 50 patients in each group. The general practice+AI group uses the assisted diagnosis system for chest diseases (ADSC) to identify X-ray images and assist general practitioners in making disease diagnoses. The control group receives diagnosis and treatment according to routine procedures, records the time spent by the two groups of doctors for comparison, and uploads the X-ray images of the two groups of patients to the regional imaging center for review and evaluation of the diagnostic accuracy of the two groups of doctors; Conduct a satisfaction survey on patients and evaluate the ADSC usage experience of two groups of doctors.

The gender, age, education, and professional title of the two groups of doctors were compared, and there was no statistically significant difference (P>0.05) . The gender and age of the two groups of patients were compared, and the difference was not statistically significant (P>0.05) . The time required for diagnosis in the general practice+AI group was significantly shorter than that in the control group [ (92.47 ± 24.52) s vs (249.31 ± 56.46) s, P<0.001], and the diagnostic accuracy was higher than that in the control group (96% vs 72%, P=0.002 7) . The satisfaction of patients in the general practice+AI group was significantly higher than that in the control group (98% vs 84%, P=0.036) . In terms of user experience evaluation of ADSC, ≥80% of general practitioners believed that it was meaningful in terms of convenience (81.25%) , diagnostic decision-making (93.75%) , professional knowledge assistance (87.50%) , and application feasibility (87.50%) . 93.75% of general practitioners were satisfied with the use of the system and willing to continue using it.

The artificial intelligence assisted diagnosis system for chest diseases has significantly improved the diagnostic efficiency, accuracy, and patient satisfaction of general practitioners in primary healthcare institutions, and most doctors hold a positive attitude towards the use of ADSC.

在部分基层医疗卫生机构中，由于放射科医生配备不足且无法保证全天候在岗，而全科医生对胸部影像的解读能力有限，影响了医疗机构服务质量与效率。

探讨人工智能胸部疾病辅助诊断系统在基层医疗卫生机构中的应用效果，重点评估其在提升全科医生诊断效率、准确性及患者满意度方面的作用。

于2024年4月，在北京市东城区东花市社区卫生服务中心选取16名全科医生为研究对象，采用随机分组法，分为全科+AI组8名、对照组8名，同时纳入两组医生接诊的共100例需X光影像检查的呼吸系统疾病患者，每组各50例，全科+AI组采用胸部疾病辅助诊断系统（ADSC）识别X光影像，辅助全科医生做出疾病诊断，对照组按照常规流程诊治，记录两组医生所用时间并进行对比，将两组患者X光影像上传区域影像中心进行阅片审核，评估两组医生诊断准确性；对患者进行满意度调查，对两组医生进行ADSC使用体验评价。

两组医生的性别、年龄、学历、职称比较，差异无统计学意义（P>0.05），两组患者的性别、年龄比较，差异无统计学意义（P>0.05）。全科+AI组诊断所需时间低于对照组[（92.47±24.52）s比（249.31±56.46）s，P<0.001]，诊断准确率高于对照组（96%比72%，P=0.002 7），全科+AI组患者满意度高于对照组（98%比84%，P=0.036），两组医生ADSC使用体验评价，≥80%的全科医生认为其在使用便捷性（81.25%）、诊断决策（93.75%）、专业知识助益（87.50%）、应用可行性（87.50%）方面具有意义，93.75%的全科医生对系统使用感到满意，并愿意继续使用该系统。

人工智能胸部疾病辅助诊断系统显著提升了基层医疗卫生机构全科医生的诊断效率、准确性和患者满意度，且大多数医生对ADSC的使用持积极态度。