NLM IRP Seminar Schedule
UPCOMING SEMINARS
RECENT SEMINARS
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July 23, 2024 Yu group
Yu Group Research Update -
July 18, 2024 Xiaofang Jiang
Jiang Lab research updates -
May 30, 2024 Deepak Gupta
Towards Answering Health-related Questions from Medical Videos: Datasets and Approaches -
May 28, 2024 Harutyun Saakyan
Simulation of protein fold evolution with atomistic details -
May 23, 2024 Leslie Ronish
Identification of fold-switching proteins by FLIM-FRET
Scheduled Seminars on April 2, 2024
Contact NLM_IRP_Seminar_Scheduling@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.