Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted.
The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit cc.nih.gov.
Updates regarding government operating status and resumption of normal operations can be found at opm.gov.
NLM DIR Seminar Schedule
UPCOMING SEMINARS
-
Nov. 13, 2025 Leslie Ronish
TBD -
Nov. 18, 2025 Ryan Bell
TBD -
Nov. 24, 2025 Mario Flores
AI Pipeline for Characterization of the Tumor Microenvironment -
Nov. 25, 2025 Jing Wang
TBD -
Dec. 2, 2025 Qingqing Zhu
TBD
RECENT SEMINARS
Scheduled Seminars on April 2, 2024
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
The promise of artificial intelligence (AI) in healthcare, from diagnosis to treatment optimization, is undeniable. However, as AI technologies like LLMs and medical imaging AI become integral to clinical practices, their inherent biases pose significant challenges. These biases can exacerbate healthcare disparities, making the pursuit of equity in AI applications not just a technical challenge but a moral imperative.
Our talk will cover two studies. The first one reveals biases in language models predicting healthcare outcomes, showing a tendency to replicate societal disparities in treatment recommendations and prognoses. The second one addresses fairness in medical imaging AI, introducing a causal fairness module that improves equity by adjusting for biases related to sensitive attributes without compromising diagnostic performance.
Addressing biases in AI is crucial for ensuring these technologies serve all patients fairly, regardless of their background. Our studies highlight the importance of continual assessment and adjustment of AI models to reflect ethical considerations alongside technical advancements.