AI for Healthcare: Scaling Access and Quality of Care for Everyone
Half of the world’s population lack access to healthcare services. Just in the US alone, 30% of the working-age adult population have inadequate health insurance coverage to get even basic access to services. Meanwhile, the healthcare system is known to have large inefficiencies that current technology hasn’t been able to address. In this talk, we will describe how our work on combining latest AI advances with medical experts and online access has the huge potential to change the landscape in healthcare access and provide 24/7 quality healthcare.
The talk will have two parts. The first part focuses on our research in areas such as NLP and medical diagnosis; Using our research in medical diagnosis as a running example, the talk will emphasize the necessary properties for the machine learned models to be effective in realistic settings to assist doctors. The second part of the talk focuses on integration of research into product and building a machine learning feedback loop. Here, we will describe the unique challenges in deploying doctor-facing AI/ML models and how we overcome them for successful adoption.
Sindhu Raghavan leads the machine learning team at Curai, a health-tech startup using AI to provide the world’s best healthcare to everyone. Prior to joining Curai, Sindhu has held research and engineering positions at Netflix and Samsung Research. She holds a PhD in machine learning and natural language processing from the University of Texas at Austin. Her interests and work spans across several areas of machine learning including statistical relational learning, recommender systems, natural language processing, and deep learning.