How Biased Data Shapes Healthcare AI | Tomisin Adebari | TEDxMorgan State University
Artificial intelligence is transforming healthcare, but what happens when the data behind these systems carries the same biases found in society?In this compelling talk, Tomisin Adebari explores how biased data can shape the decisions made by healthcare artificial intelligence systems. While AI has the potential to improve diagnoses, treatment planning, and patient outcomes, it is only as reliable as the data used to train it. When that data lacks diversity or reflects existing inequalities, the technology can unintentionally reinforce disparities rather than solve them.
Tomisin explains how these hidden biases can affect real medical decisions and patient care. From diagnostic tools to predictive health models, the consequences of incomplete or unrepresentative data can have serious implications for communities that are already underserved.
This talk challenges us to think critically about the technology we trust with human health. As healthcare continues to integrate artificial intelligence, ensuring fairness, accountability, and inclusive data becomes essential.
The future of healthcare AI depends not only on innovation, but on our commitment to building systems that work for everyone. Tomisin Adebari is a Biomedical Engineering Master's student at Johns Hopkins University, specializing in Computational Medicine. Originally from Nigeria, she is a proud Morgan State alumna whose research spans systems biology, AI in medicine, and oncology-related applications. Passionate about health equity, she works at the intersection of computational research and clinical impact. Aspiring to become a physician-engineer, Tomisin is committed to building healthcare solutions that are both technologically advanced and socially just. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx Receive SMS online on sms24.me
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