Northwestern Medicine Launches New Center for Collaborative AI in Healthcare

The Institute for Augmented Intelligence in Medicine (I.AIM) at Northwestern University Feinberg School of Medicine has established the Center for Collaborative AI in Healthcare, which aims to advance artificial intelligence (AI) science, engineering, and translation across healthcare specialties and bolster precision medicine.

According to the press release, the new center will serve as a hub for research, training, and collaboration to help clinicians, hospital administrators, and researchers apply AI to their work and clinical practice.

“There is a major unmet need for enabling a multidisciplinary workforce that interacts synergistically in the dynamic healthcare landscape. We aim to provide fertile ground for cross-pollinating next-generation clinicians and scientists to bring AI in healthcare to fruition,” said Yuan Luo, PhD, associate professor of preventive medicine and pediatrics, faculty member at the McCormick School of Engineering, and chief AI officer at the Northwestern Clinical and Translational Sciences (NUCATS) Institute and the Institute for Augmented Intelligence in Medicine, in the press release.

Luo will lead the new center, where Northwestern investigators will develop comprehensive AI and machine learning (ML) models, alongside other resources, to support the integration of multiple healthcare data modalities; build datasets that integrate both phenotypic and multi-omic data modalities; provide educational, mentoring, and training opportunities; and support the integration of data assets from other Feinberg centers.

“The new center will democratize access to the created and maintained infrastructure and training resources and engage new people previously facing AI and machine learning barriers, whether they are clinicians, hospital administrators, trainees and basic scientists both inside and outside the medical school,” Luo said.

This is Northwestern’s latest foray into the healthcare AI and data analytics space following multiple efforts to leverage the technologies to investigate COVID-19 and its impacts.

A few months after the pandemic began, a team of Northwestern University researchers developed an AI platform to quickly identify research that has the most potential to produce COVID-19 treatments and solutions. The algorithm was designed to predict which studies’ results were likely replicable, which the researchers noted is a sign that a study’s results are valid.

Thus, the tool aimed to help the research community and policymakers prioritize time and funds on studies with a high likelihood of success.

Earlier this year, researchers from the Feinberg School of Medicine published a viewpoint article analyzing the use of ML during the pandemic. They stated that healthcare stakeholders’ high expectations for the use of the technology had remained largely unrealized because of issues related to data bias, data shift, and ethological limitations.

These challenges and the limited contributions of ML show that ML best practices during the pandemic and moving forward must be re-examined. Further, a shift from reactive to proactive ML is needed, the authors concluded.