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

以人为本的临床人工智能设计支持小儿鞍上肿瘤的诊断

• 批准号: 10750837
• 负责人: Eric W Prince
• 金额: $ 3.63万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-03 至 2026-07-02
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10750837
• 关键词: Academia; Adamantinomatous craniopharyngioma; Address; Adoption; Artificial Intelligence; Attention; Bayesian Method; Brain Neoplasms; Childhood; Classification; Clinical; Clinical Medicine; Clinical effectiveness; Complex; Computer software; Consumption; Data; Data Set; Decision Making; Development; Diagnosis; Diagnostic; Diagnostic Errors; Empathy; Eye; Factor Analysis; Faculty; Goals; Health Care Costs; Human; Human Resources; Industry; Institution; Interdisciplinary Study; Knowledge; Lead; Leadership; Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Medical; Medicine; Mentors; Methods; Modeling; Oncology; Operative Surgical Procedures; Outcome; Patient Care; Patients; Performance; Persons; Positioning Attribute; Problem Solving; Process; Provider; Publishing; Reporting; Research; Research Personnel; Resource Sharing; Role; Scientist; Self-Help Devices; Services; Social Sciences; Stress; Surgical Pathology; Surveys; System; Technology; Testing; Therapeutic Intervention; Time; Training; Translating; Translations; Uncertainty; Variant; Visualization; Work; artificial intelligence method; career; clinical application; clinical decision support; clinical decision-making; complex data; computer science; cost; deep learning; deep learning model; design; experience; fictional works; human centered design; improved; information seeking behavior; mathematical ability; multidisciplinary; neuro-oncology; neurosurgery; noninvasive diagnosis; open source; pressure; prototype; radiological imaging; satisfaction; skills; success; tool; tumor; tumor diagnosis; usability

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.

项目总结/文摘