人工智能技术辅助麻醉科低年资住院医师制订术前麻醉方案的可行性

Feasibility of utilizing artificial intelligence to assist junior anesthesia residents in making preoperative anesthesia plans

目的评价人工智能技术辅助麻醉科低年资住院医师制订术前麻醉方案的可行性。方法选取扬州地区取得执业医师资格证的40名麻醉规培三年级学员,采用随机数字表法分为4组(n=10):Chat-GPT联合Bing chat组(C-G-B组)、Chat-GPT组(C-G组)、Bing chat组(B组)和对照组(C组)。通过麻醉门诊筛选出50例择期行非心脏手术患者作为教学病例,C-G-B组、C-G组、和B组规培学员应用不同的人工智能工具辅助制订术前麻醉方案,并形成标准化的文字输出。入组前应用专业理论考试对每位学员进行学科基线成绩检测,由3名麻醉科主任医师对术前麻醉方案制订的完整性与准确性进行评分。记录麻醉方案制订的总耗时以及对人工智能工具反馈信息的满意度评分,并根据教学病例的ASA分级进行逐层分析。结果在ASA分级Ⅰ和Ⅱ级教学病例中,4组完整性和准确性评分比较差异无统计学意义(P>0.05);在ASA分级Ⅲ级教学病例中,与C组比较,C-G-B组、C-G组和B组完整性和准确性评升高,且C-G-B组完整性和准确性评分最高(P<0.05)。在所有教学病例中(ASA分级Ⅰ-Ⅲ级),C-G组和B组较C组和C-G-B组总耗时缩短(P<0.05),C-G组与B组、C组和C-G-B组总耗时比较差异无统计学意义(P>0.05);与C-G组和B组比较,C-G-B组满意度评分降低(P<0.05)。结论对于ASA分级Ⅲ级的患者,采用人工智能辅助麻醉科低年资住院医师制订术前麻醉方案可能更具优势,虽然联合应用Chat-GPT和Bing chat可进一步提升麻醉方案制订的完整性和准确性,但耗时较长。

Objective To evaluate the feasibility of utilizing artificial intelligence(AI)to assist junior anesthesia residents in making the preoperative anesthesia plans.Methods Forty anesthesia residents in their third year of training,who had obtained their practicing physician qualifications in the Yangzhou area,were assigned into 4 groups(n=10 each)using a random number table method:Chat-GPT combined with Bing chat group(C-G-B group),Chat-GPT group(C-G group),Bing chat group(B group),and control group(C group).Fifty patients undergoing elective non-cardiac surgery were selected from the anesthesia clinic as teaching cases.C-G-B,C-G and B groups utilized different AI tools to assist trainees in designing anesthesia plans,producing standardized textual outputs.Each trainee underwent a baseline knowledge test through a professional theory examination prior to enrollment.The completeness and accuracy of the preoperative anesthesia plans were evaluated and scored by 3 chief anesthesiologists.The total time spent on plan formulation and satisfaction scores regarding AI tool feedback were recorded.An analysis was conducted based on the American Society of Anesthesiologists(ASA)Physical Status classification of teaching cases.Results In ASA Physical Status classificationⅠandⅡteaching cases,there was no statistically significant difference in completeness and accuracy scores among the four groups(P>0.05).In ASA Physical Status classificationⅢteaching cases,compared to C group,the completeness and accuracy scores were significantly increased in C-G-B,C-G and B groups,with the highest scores observed in C-G-B group(P<0.05).Among all teaching cases(ASA Physical Status classificationⅠ-Ⅲ),the total time spent was significantly shortened in C-G and B groups as compared to C and C-G-B groups(P<0.05).There was no statistically significant difference in the total time spent between C-G group and C-G-B group(P>0.05).Compared to C-G and B groups,the satisfaction score was significantly decreased in C-G-B group(P<0.05).Conclusions For ASA Physical Status classificationⅢpatients,using AI to assist junior anesthesia residents in making preoperative anesthesia plans may offer advantages.Although combining the use of Chat-GPT and Bing chat can further improve the completeness and accuracy of anesthesia plan development,it may require more time.