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New York, Dec. 18, 2024 — Two studies published at the prestigious NeurIPS conference highlight significant developments in artificial intelligence (AI) that could transform healthcare. One focuses on enhancing personalized treatment strategies through reinforcement learning (RL), while the other adapts cutting-edge image-processing AI to interpret complex graph data, such as brain and gene networks.
The first study, conducted by researchers at Weill Cornell Medicine and Rockefeller University, unveils a groundbreaking RL framework called “Episodes of Care” (EpiCare). The research suggests that RL has the potential to guide physicians in designing sequential treatment plans, optimizing care for patients with chronic or psychiatric conditions. RL, a type of machine learning, has shown promise in fields like gaming, where AI systems have achieved superhuman performance in games such as chess and Go. The new study, published in the Proceedings of NeurIPS, suggests that RL could evolve to manage dynamic, long-term treatment decisions in healthcare, where patient conditions and responses change over time.
“Benchmarks have spurred major advancements across AI applications like self-driving cars and natural language processing,” explained Dr. Logan Grosenick, assistant professor of neuroscience in psychiatry and lead researcher. “We hope that EpiCare will push RL progress in healthcare, ensuring more personalized care for patients.”
Despite its promise, RL in healthcare faces significant hurdles. The study’s results indicate that while RL models can outperform baseline treatment methods, they require vast amounts of simulated data for training—an impractical requirement for real-world clinical applications. The researchers also found that existing methods used to predict RL’s effectiveness based on historical data, known as “off-policy evaluation” (OPE), were inaccurate when applied to healthcare scenarios. These findings underline the need for new, more reliable benchmarking tools like EpiCare to improve the real-world utility of RL in medicine.
Dr. Grosenick added, “Our work aims to enhance the reliability of RL applications in healthcare, paving the way for algorithms that can be directly used for patient care.”
In the second study, Dr. Grosenick and his team introduced a novel adaptation of convolutional neural networks (CNNs), which have been widely successful in processing images, to work with graph-structured data such as brain networks, gene interactions, and protein structures. CNNs have been instrumental in various fields, including medical imaging and facial recognition. However, these networks are less effective when applied to data represented as graphs—networks of interconnected elements, such as neurons in the brain.
The new framework, called Quantized Graph Convolutional Networks (QuantNets), adapts CNNs to analyze and interpret graph data more effectively. This innovation could revolutionize the study of brain activity, gene expression, and other complex biological data, leading to more accurate models of disease and treatment. The research team has already applied QuantNets to EEG data, helping to model how brain connectivity changes in response to treatments for conditions like depression and obsessive-compulsive disorder.
“This approach opens up new possibilities for understanding dynamic, complex systems in biology,” said Isaac Osafo Nkansah, first author of the study. “By reducing large, complex graphs into more interpretable components, we can better understand the underlying patterns and connections that drive diseases and treatments.”
Dr. Grosenick emphasized that while applying these advanced AI methods to patient care is still in its early stages, the development of reliable benchmarking frameworks and more accurate models is a crucial step toward personalized treatment strategies. “Every advancement brings us closer to improving patient outcomes,” he concluded.
Both studies illustrate the transformative potential of AI in healthcare, offering new tools for managing complex diseases and personalizing care. However, challenges remain in refining these technologies to ensure their effectiveness in real-world clinical settings.
For more information, refer to the studies:
- Mason Hargrave et al., EpiCare: A Reinforcement Learning Benchmark for Dynamic Treatment Regimes (2024)
- Isaac Osafo Nkansah et al., Generalizing CNNs to Graphs with Learnable Neighborhood Quantization (2024)
