Lin Yannan, Hoyt Anne C, Manuel Vladimir G, Inkelas Moira, Maehara Cleo K, Ayvaci Mehmet Ulvi Saygi, Ahsen Mehmet Eren, Hsu William
Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
AMIA Annu Symp Proc. 2025 May 22;2024:713-722. eCollection 2024.
Artificial intelligence (AI) shows promise in clinical tasks, yet its integration into workflows remains underexplored. This study proposes an AI-aided same-day diagnostic imaging workup to reduce recall rates following abnormal screening mammograms and alleviate patient anxiety while waiting for the diagnostic examinations. Using discrete simulation, we found minimal disruption to the workflow (a 4% reduction in daily patient volume or a 2% increase in operating time) under specific conditions: operation from 9 am to 12 pm with all radiologists managing all patient types (screenings, diagnostics, and biopsies). Costs specific to the AI-aided same-day diagnostic workup include AI software expenses and potential losses from unused pre-reserved slots for same-day diagnostic workups. These simulation findings can inform the implementation of an AI-aided same-day diagnostic workup, with future research focusing on its potential benefits, including improved patient satisfaction, reduced anxiety, lower recall rates, and shorter time to cancer diagnoses and treatment.
人工智能（AI）在临床任务中显示出前景，但其融入工作流程的情况仍未得到充分探索。本研究提出一种人工智能辅助的当日诊断成像检查流程，以降低异常筛查乳腺X线摄影后的召回率，并在患者等待诊断检查时减轻其焦虑。通过离散模拟，我们发现在特定条件下对工作流程的干扰最小（每日患者量减少4%或操作时间增加2%）：上午9点至中午12点运营，所有放射科医生管理所有类型的患者（筛查、诊断和活检）。人工智能辅助当日诊断检查流程的特定成本包括人工智能软件费用以及当日诊断检查未使用的预预留时段的潜在损失。这些模拟结果可为人工智能辅助当日诊断检查流程的实施提供参考，未来研究将聚焦于其潜在益处，包括提高患者满意度、减轻焦虑症、降低召回率以及缩短癌症诊断和治疗时间。
