Artificial intelligence (AI) applications hold the key to reducing healthcare inequity, both locally and globally. While centralized architectures have been instrumental in advancing consumer AI, they are inadequate to deploy and train AI applications for children’s medicine. Amidst the pandemic-induced shutdown, a small team gathered to establish a new company with the mission of creating privacy-preserving, real-time applications based on data access from all 1,000,000 healthcare machines in the 500 children’s hospitals worldwide.
This session will give an overview of the mission, a new “rocket”, BevelCloud’s decentralized, in-the-building, edge cloud service, and its application to pediatric medicine. Our speaker, Timothy Chou, will discuss how AI applications are key to reducing healthcare inequity both locally and globally.
At the end of the talk, Raja Iqbal, our Chief Data Scientist, will lead a Q&A session with Tim.
We are looking for passionate people willing to cultivate and inspire the next generation of leaders in tech, business, and data science. If you are one of them get in touch with us!
The article, “Why AI for Children’s Medicine?” by Timothy Chou, discusses the benefits of using AI in pediatric medicine, such as improving diagnostic accuracy, reducing medical errors, and personalizing treatment. The article emphasizes the importance of collecting high-quality data and developing algorithms that are specifically tailored to pediatric populations.
The article, “Why have we seen Rapid Advances in consumer AI but not AI in Medicine?” also by Timothy Chou, explores the reasons why consumer AI has made more progress than AI in medicine. The author argues that the healthcare industry faces unique challenges, such as strict regulations, a lack of data interoperability, and the need for specialized expertise, that make it more difficult to implement AI in a clinical setting.
Why Centralized Architectures are not the answer for AI in Medicine
The article, “Why Centralized Architectures are not the answer for AI in Medicine,” explains the limitations of centralized architectures in healthcare, such as data privacy concerns and the potential for bias. The author argues that decentralized architectures, such as federated learning, offer a better solution for AI in medicine.
The article, “A decentralized architecture for AI in Medicine,” continues the discussion on decentralized architectures and explores the benefits of using federated learning for medical applications. The article describes how federated learning allows multiple parties to collaborate on AI models without compromising data privacy.
The article, “Federated Learning for Consumer AI,” discusses how federated learning can be applied to consumer AI applications, such as personal assistants and smart home devices. The article emphasizes the importance of data privacy and describes how federated learning allows user data to be kept private.
Federated Learning Lab for Children’s Medicine
The article, “Federated Learning Lab for Children’s Medicine,” describes a proposed federated learning platform for pediatric medicine that would allow hospitals to collaborate on AI research while maintaining data privacy.
The article, “Translate AI Research from the Bench to the Bedside,” discusses the challenges of translating AI research into clinical practice. The author argues that researchers must work closely with clinicians to ensure that AI algorithms are clinically relevant and can be integrated into the healthcare system.