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How AI, Machine Learning Are Providing an Edge on Interstitial Lung Disease

Simon Walsh, MD, and Elizabeth Estes join Lungcast to discuss advancements in biomarker and treatment option deliberation for the rare disease through data.

Artificial intelligence (AI) and deep machine learning, previously theoretical proposals of the future of industries, are making a real-time impact in pockets of medicine. In the case of advancing care and diagnostics for a collection of rare, chronic lung diseases, these rapidly evolving tools may make a world of difference today.

In this month’s episode of Lungcast, American Lung Association (ALA) chief medical officer Albert Rizzo, MD, discusses the advancement of AI and data learning in the 200-plus interstitial lung diseases (ILDs) including idiopathic pulmonary fibrosis (IPF).

He’s joined by Simon Walsh, MD, a consultant thoracic radiologist and clinician scientist with the National Heart and Lung Institute at Imperial College in London, and Elizabeth Estes, executive director of the Open Source Imaging Consortium (OSIC).

Walsh and Estes walk Rizzo through the founding of OSIC, and the role itself and fellow data networks are providing to the various clinicians and researchers looking for an upperhand on the burdensome and elusive effects of ILDs on patients.

Prior to their conversation, Rizzo also reviews the centuries-long history of applied AI, beginning with the illustrious career of mathematician Alan Turing.

Lungcast is a monthly respiratory health podcast series from the ALA produced by HCPLive.

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