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

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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.