The legacy and future of recurrent neural networks in personalized medicine: A reflection on the 2024 Nobel Physics Prize.
Authors
Affiliations (5)
Affiliations (5)
- Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. Electronic address: [email protected].
- Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. Electronic address: [email protected].
- Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. Electronic address: [email protected].
- College of Intelligent Computing, Chang Gung University, Taoyuan City, Taiwan; Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung City, Taiwan; Max Harry Weil Institute for Critical Care Research and Innovation, Michigan Medicine, Ann Arbor, MI, USA. Electronic address: [email protected].
- Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Max Harry Weil Institute for Critical Care Research and Innovation, Michigan Medicine, Ann Arbor, MI, USA; Michigan Institute for Data and AI in Society (MIDAS), University of Michigan, Ann Arbor, MI, USA; Center for Data-Driven Drug Development and Treatment Assessment (DATA), University of Michigan, Ann Arbor, MI, USA. Electronic address: [email protected].
Abstract
The 2024 Nobel Physics Prize was awarded to Geoffrey Hinton and John Hopfield for their pioneering contributions to neural networks and artificial intelligence (AI), marking a significant milestone in AI's development, particularly in the potential integrations into personalized medicine. This article surveys the profound influence of Hopfield's and Hinton's foundational work, tracing the development of recurrent neural networks (RNNs) from early associative memory models to advanced deep learning architectures. We delve into how contemporary RNN architectures are transforming personalized medicine by improving diagnostic accuracy, facilitating image analysis, generating radiology reports, and estimating individual treatment effects. Despite advancements, current challenges such as model interpretability, generalizability, and ethical considerations in AI application demand further exploration. This article posits that future RNN development will blend rigorous algorithmic insights with powerful generative capabilities to advance both medical applications and theoretical understanding. We conclude with a reflection on the future trajectory of RNNs in AI, underscoring a need for balancing computational efficiency with transparency and adaptability in healthcare environments.