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Washington University in St. Louis Launches AI for Health Institute

The AI for Health Institute is set to develop data-driven tools to advance precision medicine, characterize complex diseases, and support clinical decision-making.

AI in healthcare

Source: Getty Images

By Shania Kennedy

- The McKelvey School of Engineering at Washington University in St. Louis has established the AI for Health Institute, which will bring together clinical researchers and artificial intelligence experts to help tackle complex medical issues.

The institute, launched last week at Washington University’s AI & Digital Health Summit, aims to help research teams design advanced tools to support clinical decision-making, bolster precision health, and characterize complex diseases.

The AI for Health Institute is also intended to make Washington University a leader in health AI.

“This is the new frontier of health care,” said Chenyang Lu, PhD, the Fullgraf Professor of Computer Science & Engineering in the McKelvey School of Engineering and director of the institute, in the press release. “Given the complexity of the problems and the messiness of the data, basic AI tools are insufficient to solve a lot of these problems. That’s where cutting-edge AI comes in.”

While AI tools and techniques are being rapidly developed, barriers between engineering, health, and AI researchers have hindered health AI development. To remedy this, AI for Health Institute stakeholders will collaborate with teams from the Institute for Informatics, Data Science, and Biostatistics (I2DB) at the Washington University School of Medicine.

“The new AI for Health Institute represents an exciting opportunity to more formally connect the School of Engineering with what I2DB is doing at the School of Medicine, facilitating cross-cutting collaborations around AI and IoT, particularly at this pivotal moment where we have a tremendous opportunity to impact the greater good and the overall health of our communities,” explained Philip R. O. Payne, PhD, FACMI, FAMIA, FAIMBE, FIAHSI, the Janet and Bernard Becker Professor, associate dean for health information and data science, and chief data scientist at the School of Medicine and professor of computer science & engineering in McKelvey Engineering.

Lu noted that the institute’s leadership plans to foster expanded collaboration across teams in the health and engineering communities, establish infrastructure for large research initiatives, and develop a competitive edge in recruitment to help advance AI in healthcare.

Initially, the institute will support interdisciplinary research in four core areas: equity, fairness, and privacy in AI; natural language processing (NLP); imaging AI; and wearables for healthcare.

For now, projects undertaken by researchers at the AI for Health Institute will focus on telemedicine and critical care, perioperative care, mental healthcare, reproductive healthcare, neurosurgery, digital pathology, and infectious diseases.

More cores and focus areas will be added as the institute grows.

“The AI for Health Institute will provide outstanding resources to accelerate improvements in health by harnessing big data, machine learning technology and artificial intelligence for use in health care delivery and health services research,” said Victoria J. Fraser, MD, the Adolphus Busch Professor of Medicine and chair of the Department of Medicine at the School of Medicine.

Other institutions are also looking for ways to advance AI in healthcare.

Last week, Mayo Clinic Platform_Accelerate announced the newest cohort of health tech startups to participate in its 20-week program, which is designed to help each company develop and validate its AI tools.

The program provides cohort members with access to May Clinic experts across business, technological, clinical, and regulatory realms. In addition, the nine startups also receive resources to support the clinical readiness of their AI solutions.

This cohort is building AI tools to advance cancer risk stratification, personalized medicine, heart failure care, sepsis identification, health equity, ICU automation, early detection of disease, post-COVID care, and cardiac diagnostics.