Abstract：
BACKGROUND: OpenAI\'s GPT-4 (artificial intelligence [AI]) is being studied for its use as a medical decision support tool. This research examines its accuracy in refining referrals for fetal echocardiography (FE) to improve early detection and outcomes related to congenital heart defects (CHDs).
METHODS: Past FE data referred to our institution were evaluated separately by pediatric cardiologist, gynecologist (human experts [experts]), and AI, according to established guidelines. We compared experts and AI\'s agreement on referral necessity, with experts addressing discrepancies.
RESULTS: Total of 59 FE cases were addressed retrospectively. Cardiologist, gynecologist, and AI recommended performing FE in 47.5%, 49.2%, and 59.0% of cases, respectively. Comparing AI recommendations to experts indicated agreement of around 80.0% with both experts (p < 0.001). Notably, AI suggested more echocardiographies for minor CHD (64.7%) compared to experts (47.1%), and for major CHD, experts recommended performing FE in all cases (100%) while AI recommended in majority of cases (90.9%). Discrepancies between AI and experts are detailed and reviewed.
CONCLUSIONS: The evaluation found moderate agreement between AI and experts. Contextual misunderstandings and lack of specialized medical knowledge limit AI, necessitating clinical guideline guidance. Despite shortcomings, AI\'s referrals comprised 65% of minor CHD cases versus experts 47%, suggesting its potential as a cautious decision aid for clinicians.
METHODS: Past FE data referred to our institution were evaluated separately by pediatric cardiologist, gynecologist (human experts [experts]), and AI, according to established guidelines. We compared experts and AI\'s agreement on referral necessity, with experts addressing discrepancies.
RESULTS: Total of 59 FE cases were addressed retrospectively. Cardiologist, gynecologist, and AI recommended performing FE in 47.5%, 49.2%, and 59.0% of cases, respectively. Comparing AI recommendations to experts indicated agreement of around 80.0% with both experts (p < 0.001). Notably, AI suggested more echocardiographies for minor CHD (64.7%) compared to experts (47.1%), and for major CHD, experts recommended performing FE in all cases (100%) while AI recommended in majority of cases (90.9%). Discrepancies between AI and experts are detailed and reviewed.
CONCLUSIONS: The evaluation found moderate agreement between AI and experts. Contextual misunderstandings and lack of specialized medical knowledge limit AI, necessitating clinical guideline guidance. Despite shortcomings, AI\'s referrals comprised 65% of minor CHD cases versus experts 47%, suggesting its potential as a cautious decision aid for clinicians.
摘要：
OpenAI的GPT-4（人工智能（AI））正在研究其作为医疗决策支持工具的用途。这项研究检查了其在完善胎儿超声心动图（FE）转诊中的准确性，以改善与先天性心脏缺陷相关的早期发现和结局。方法由儿科心脏病专家单独评估我们机构的过去FE数据，妇科医生（人类专家（专家）），AI,根据既定的指导方针。我们比较了专家和人工智能关于转诊必要性的协议，与专家解决差异。结果对59例FE病例进行回顾性分析。心脏病专家,妇科医生,AI建议在47.5%中执行FE，49.2%,59.0%的病例，分别。将AI建议与专家进行比较，与两位专家的一致性约为80.0%(p<0.001)。值得注意的是,与专家（47.1％）相比，AI建议对轻度冠心病（64.7％）进行更多的超声心动图检查，对于主要的冠心病，专家建议在所有情况下进行FE（100％），而在大多数情况下推荐AI（90.9％）。AI和专家之间的差异进行了详细的审查。结论评估发现AI与专家之间存在适度的一致性。背景误解和缺乏专业医学知识限制了人工智能，需要临床指南指导。尽管有缺点,AI的推荐占次要CHD病例的65%，专家占47%，表明其作为临床医生谨慎决策辅助的潜力。
