Evaluating Use of New AI Technology in Diagnosing COVID-19

Published in the Journal of the American Medical Informatics Association, University of Minnesota researchers led a study evaluating federated learning variations for COVID-19 diagnosis in chest x-rays. Federated learning is an artificial intelligence (AI) technique that enables multiple parties to develop and train AI models collaboratively without the need to exchange or centralize data sets.

This research is a collaboration between the U of M, M Health Fairview, Emory University, Indiana University School of Medicine and University of Florida.

The research team compared the performance of a single site to a three-client federated model using a previously described COVID-19 diagnostic model. They found personalized federated learning may offer an opportunity to develop both internal and externally validated algorithms.

"Federated learning is an important future solution for AI in healthcare," Christopher Tignanelli, MD, MS, an associate professor at the University of Minnesota Medical School. "As all machine learning methods benefit greatly from the ability to access data that provides closer to a true global distribution, federated learning is a promising approach to obtain powerful, accurate, safe, robust and unbiased models."

Dr. Tignanelli co-led this study with Ju Sun, PhD, an assistant professor in the College of Science and Engineering. Both are leaders of the Program for Clinical AI in the Center for Learning Health System Sciences at the U of M Medical School.

"We're proud to be among the first teams implementing and further refining federated learning in real-world healthcare settings, with the strong support of industrial partners including Nvidia and Cisco," said Sun. "Data is the oil for modern AI, and federated learning makes the perfect oil refinery to advance AI for healthcare."

By enabling multiple parties to train collaboratively without the need to exchange or centralize data sets, the research team says federated learning helps protect sensitive medical data and may open new research and business avenues to improve patient care.

State-of-the-art algorithms are usually evaluated on carefully curated data sets originating from only a few sources, rather than truly representative data. This can introduce biases where demographics or technical imbalances skew predictions and adversely affect the accuracy for certain groups or sites. Researchers say to capture subtle relationships between disease patterns, socio-economic and genetic factors, and complex and rare cases, it is crucial to expose a model to diverse cases.

The research team says other potential benefits of federated learning include:

  • Improved medical image and text analysis;
  • Better diagnostic tools for clinicians;
  • Collaborative and accelerated drug discovery;
  • Decreased cost and time-to-market for pharmaceutical companies;
  • Rare disease cases where no single institution has enough cases to train models.

"We truly believe the potential impact on precision medicine and ultimately improving medical care is very promising," said Dr. Tignanelli.

Further research is suggested to address more technical questions in using this technology. Part of the funding for this research was provided by Cisco.

Le Peng, Gaoxiang Luo, Andrew Walker, Zachary Zaiman, Emma K Jones, Hemant Gupta, Kristopher Kersten, John L Burns, Christopher A Harle, Tanja Magoc, Benjamin Shickel, Scott D Steenburg, Tyler Loftus, Genevieve B Melton, Judy Wawira Gichoya, Ju Sun, Christopher J Tignanelli.
Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.
Journal of the American Medical Informatics Association, 2022. doi: 10.1093/jamia/ocac188

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...