TY - JOUR
T1 - Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system
AU - Luo, Yuan
AU - Mao, Chengsheng
AU - Sanchez-Pinto, Lazaro N.
AU - Ahmad, Faraz S.
AU - Naidech, Andrew
AU - Rasmussen, Luke
AU - Pacheco, Jennifer A.
AU - Schneider, Daniel
AU - Mithal, Leena B.
AU - Dresden, Scott
AU - Holmes, Kristi
AU - Carson, Matthew
AU - Shah, Sanjiv J.
AU - Khan, Seema
AU - Clare, Susan
AU - Wunderink, Richard G.
AU - Liu, Huiping
AU - Walunas, Theresa
AU - Cooper, Lee
AU - Yue, Feng
AU - Wehbe, Firas
AU - Fang, Deyu
AU - Liebovitz, David M.
AU - Markl, Michael
AU - Michelson, Kelly N.
AU - McColley, Susanna A.
AU - Green, Marianne
AU - Starren, Justin
AU - Ackermann, Ronald T.
AU - D'Aquila, Richard T.
AU - Adams, James
AU - Lloyd-Jones, Donald
AU - Chisholm, Rex L.
AU - Kho, Abel
N1 - Publisher Copyright:
© 2024 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.
PY - 2024/7
Y1 - 2024/7
N2 - Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Results: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Conclusions: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.
AB - Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Results: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Conclusions: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.
KW - Collaborative AI in Healthcare
KW - artificial intelligence
KW - collaborative learning
KW - health workforce
KW - learning health system
KW - multimodal machine learning
KW - team science
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U2 - 10.1002/lrh2.10417
DO - 10.1002/lrh2.10417
M3 - Article
C2 - 39036530
AN - SCOPUS:85190849502
SN - 2379-6146
VL - 8
JO - Learning Health Systems
JF - Learning Health Systems
IS - 3
M1 - e10417
ER -