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WCLC 2026 | Prospective implementation of Sybil AI for lung cancer risk prediction
Evan Garrad, MD, University of Illinois Chicago, Chicago, IL, comments on the approach of the Sybil consortium in utilizing artificial intelligence (AI) tools in a research setting, highlighting the benefits of its open access model in promoting collaboration and access to diverse patient populations. Dr Garrad notes that his institution is focused on implementing Sybil in a prospective manner to assess its effectiveness in the clinic and understand its interaction with their patient population. This interview took place at the IASLC 2026 World Conference on Lung Cancer congress in Seoul, Republic of Korea.
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Transcript
Yeah, I think this is a really important question. I think our approach as part of the Sybil consortium is really to approach these tools in a research setting and research environment. I think that’s one of the great things about Sybil is an open access model. So it gives our site, which has a different diverse patient population, access to implement it in a prospective and retrospective manner, but also the whole world has access to this...
Yeah, I think this is a really important question. I think our approach as part of the Sybil consortium is really to approach these tools in a research setting and research environment. I think that’s one of the great things about Sybil is an open access model. So it gives our site, which has a different diverse patient population, access to implement it in a prospective and retrospective manner, but also the whole world has access to this. So I think it encourages a lot of collaboration on these novel AI tools. And I think that’s going to be really important here in the future. One of our focuses at UIC is to now implement Sybil in a prospective manner and see how it’s working in the clinic. And I think this gives us a lot of advantage to bringing it into clinical spaces in the future because it lets us understand how it’s interacting with our patient population.
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