Bringing AI to Medical Education at UTSW's SimCenter
UT Southwestern’s brand-new Simulation Center provides the latest technology and operational support to the UTSW Medical School, which trains one of the largest medical student cohorts in the nation. With the advent of breakthrough AI such as ChatGPT, we aim to leverage this capability of large language models (LLMs) and machine learning (ML) techniques to uncover insights and patterns in the vast amounts of data generated by the SimCenter. The ultimate goal of the project is to gain a deeper understanding of the complexities of medical simulation, improve simulation capability, and ultimately apply the knowledge to optimize medical student and professional clinical staff training. We need talented and enthusiastic software engineers, computational scientists, programmers, developers, and hackers to join us in revolutionizing the field of medical education & putting this world-changing technology in the hands of capable medical professional for a real-world, positive impact in the advancement of healthcare.
The project will encompass processing and understanding unstructured data such as medical notes, procedures, and examination of videos of simulated patient-provider encounters with analyses of non-verbal cues, interactions, and student performance under pressure. Overall, this rich data will allow an extraordinary window into human behavior and empower innovation of video-based machine learning models.
The power of the state-of-the-art LLMs will enable us to automatically deconstruct student reasoning, evaluate their decision-making, and provide targeted feedback for improvement, leading to more effective learning. LLMs will also assist in grading students based on simulation scenarios and provide an objective, accurate evaluation of their performance.
Finally, a critical component of the project is to build intuitive user interfaces for medical educators, physicians, and students. The end users will interact with the ML models, learn from the data, and simultaneously provide expert feedback for improving the models. The interfaces will allow easy navigation, visualization, and data analyses of simulation encounters, making them accessible and actionable for all roles. This will empower the medical community of doctors, educators, and learners to take active control of their training.
Data Scientist I (machine learning engineer role)
This role will employ a highly skilled data scientist with expertise in developing machine learning models to curate and analyze large, multi-modal datasets generated by the SimCenter at UT Southwestern Medical School. Experience with using machine learning for analyses of video-based data sets is a plus. They will work closely with our software and AI engineers to ensure that the models are integrated into actionable user interfaces.
The brand new SimCenter at UT Southwestern is one of the largest state-of-the-art simulation facility for medical education in the US. We invite a talented, experienced software engineer with ML/AI experience to join our team in building intuitive user interfaces for medical educators, physicians, and students. The successful candidate will have experience in building UI/UX and working with machine learning models. The role includes leveraging large language models (LLMs) into user-friendly interfaces to allow for non-ML experts to easily navigate through the simulations, enable visualization accurately, and assist data analysis in an accessible and actionable way. The engineer will work closely with end users as well as data science and machine learning teams to not only design but also test, improve, and innovatively operationalize tools and features.
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Whether you are interested specifically in the above posted jobs, or if you have expertise in natural language processing, AI, data science, software development, user interface development, computational science, etc. please reach out!