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AI for biomedicine

Published on June 30, 2026   15 min

A selection of talks on Oncology

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0:00
Hello everyone. My name is Khanh, and it is my pleasure to share with you today an overview of artificial intelligence in biomedicine. Over the next few minutes, I will introduce the core concepts, highly likely application areas, some examples from research, and then discuss some challenges, opportunities, and the future outlook.
0:26
Here's the roadmap of today's talk. First, I will define what AI in biomedicine really means. Then I will move into some of the key application areas where AI is already having an impact. I will share a few case studies from my research and others' work. After that, we will look at some challenges and open questions, and finally, I will wrap up with future directions and a take-home message.
0:58
So why has AI become so important in biomedicine right now? One major reason is the explosion of biomedical data like genomics, medical images, and electronic health records. Traditional analysis methods struggle to handle the scale and complexity of this data. AI now provides a powerful toolkit for pattern recognition, prediction, and also personalization. We have already seen some success in areas like diagnostics and also drug development tasks.
1:38
Core concepts in AI for biomedicine. Before diving into applications, let's quickly review some core concepts. AI is probably about simulating human intelligence by machine, and within AI, machine learning is about algorithms that learn from data, either labeled or unlabeled. We mentioned supervised and unsupervised learning. Deep learning is a different model. So it takes this further with neural networks that handle very complex data, complex tasks, and more recently, large language models and NLP approaches have been adopted to biological data, enabling us to read and write biological sequences much like natural language.

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