Human-centered design of clinical AI to support the diagnosis of pediatric suprasellar tumors

项目总结/摘要 儿科神经肿瘤学等亚专业的临床决策正变得越来越数据化， 驱动和复杂。人工智能（AI）是一个强大的工具，可以帮助提取这个不断扩大的数据集， 在正确的时间向临床医生提供正确的信息。人工智能在临床应用上几乎没有成功 但新的方法（如OpenAI的DALL-E或GPT 3模型）现在已经进入公众视野，清楚地表明 技术的力量。将该技术有效转化为临床环境需要 全面了解特定的临床环境，例如人员/角色，使用的数据/技术， 临床目标和工作流程。以人为中心的设计（HCD）是一个强调需求的解决方案框架。 执行特定任务的人，非常适合促进临床AI的设计。然而，HCD 在这一领域执行具有挑战性，因为组建一个专家团队既困难又昂贵， 横跨临床医学、人工智能、可视化和社会科学。学术界和工业界 建立了多学科HCD/AI团队，但正在寻求填补跨学科领导角色的解决方案 在这些团队中。 我之前发表了一个深度学习模型，用于将儿童鞍上肿瘤分类为 术前MRI。我的模型在同一数据集上的表现与人类专家一样好（86%），这也是 这与以前关于人类专家在任务上的表现的研究一致。此外，儿童鞍上 肿瘤几乎总是通过手术病理学诊断，大约8%的患者通过放射学检查 诊断。初步数据（目标2）表明，我们可以将深度学习模型的性能提高到 95%，采用贝叶斯方法来估计模型的不确定性。其他初步数据（Aim 3)表明将我的模型嵌入谷歌的假设工具（WIT）可以帮助临床医生进行放射学检查 以更小的感知困难和更大的感知信心来诊断这些肿瘤。因此，这一提议 中心假设是，可解释的人工智能解决方案可以改善人类专家的儿童鞍上肿瘤 放射学诊断超出当前性能水平。此外，使用HCD方法，Google的 假设工具（WIT）可以适应临床医生的工作流程，其方式将导致采用该 辅助技术我将调查我的目标，专门旨在提供这些方面的功能知识 主题，使我能够有效地领导一个多学科的主题专家团队， 临床AI工具我由一个专家团队指导，他们代表儿科神经肿瘤学，神经外科学， AI、可视化和HCD。完成这份提案将大大有助于我的职业目标：成为一名 应用HCD开发临床AI技术以支持儿科神经肿瘤患者的领导者 在乎

PROJECT SUMMARY/ABSTRACT Clinical decision-making in subspecialties, like pediatric neuro-oncology, is becoming increasingly data- driven and complex. Artificial intelligence (AI) is a powerful tool that can help distill this expanding dataset to present the clinician with the right information at the right time. AI has had little success in clinical applications so far, but new methods (like OpenAI's DALL-E or GPT3 models) are now in the public eye, clearly demonstrating the power of the technology generally. Effective translation of that technology into the clinical setting requires a comprehensive understanding of the specific clinical setting, such as personnel/roles, data/technology used, clinical goals and workflows. Human-Centered Design (HCD) is a solution framework that emphasizes the needs of the people who perform a specific task and is well suited to facilitate the design of clinical AI. However, HCD is challenging to execute in this space because it is difficult and expensive to assemble a team of experts that spans clinical medicine, artificial intelligence, visualization, and social sciences. Academia and industry have established multidisciplinary HCD/AI teams but are seeking solutions for filling interdisciplinary leadership roles in these teams. I previously published a deep learning model for classifying pediatric suprasellar tumors from preoperative MRI. My model performed as well as human experts on the same dataset (86%), which was also congruent with previous studies on human expert performance on the task. In addition, pediatric suprasellar tumors are almost always diagnosed via surgical pathology, with roughly 8% of patients being radiographically diagnosed. Preliminary data (Aim 2) suggests that we can improve the deep learning model performance up to 95% by incorporating a Bayesian methodology to estimate model uncertainty. Additional preliminary data (Aim 3) indicates that embedding my model into Google's What-If Tool (WIT) can help clinicians radiographically diagnose these tumors with less perceived difficulty and greater perceived confidence. Therefore, this proposal's central hypothesis is that explainable AI solutions can improve human experts' pediatric suprasellar tumor radiographic diagnosis beyond the current performance levels. Moreover, using an HCD approach, Google's What-If Tool (WIT) can be adapted into the clinician's workflow in a manner that will result in adoption of this assistive technology. I will investigate my aims, specifically designed to provide functional knowledge in these topics to enable me to effectively lead a multidisciplinary team of subject-matter experts in developing robust clinical AI tools. I am guided by an expert team of mentors representing pediatric neuro-oncology, neurosurgery, AI, visualization, and HCD. Completion of this proposal will significantly contribute to my career goal: to be a leader in the application of HCD to develop clinical AI technology that supports pediatric neuro-oncology patient care.