Review Advance Articles
Deep learning and generative artificial intelligence in aging research and healthy longevity medicine
- 1 Duke University, Durham, NC 27708, USA
- 2 Duke Kunshan University, Kunshan, Jiangsu 215316, China
Received: September 23, 2024 Accepted: January 8, 2025 Published: January 16, 2025
https://doi.org/10.18632/aging.206190How to Cite
Copyright: © 2025 Wilczok. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep aging clock development, geroprotector identification and generation of dual-purpose therapeutics targeting aging and disease. The paper explores the emergence of multimodal, multitasking research systems highlighting promising future directions for GenAI in human and animal aging research, as well as clinical application in healthy longevity medicine.
Abbreviations
AD: Alzheimer’s Disease; AI: Artificial Intelligence; CNN: Convolutional Neural Network; DAC: Deep Aging Clocks; DDPM: Denoising Diffusion Probabilistic Models; DL: Deep Learning; DNN: Deep Neural Networks; GAN: Generative Adversarial Network; GenAI: General Artificial Intelligence; GENTRL: Generative Tensorial Reinforcement Learning; LLM: Large Language Model; LSTM: Long Short Term Memory; MAE: Mean Average Error; ML: Machine Learning; P3GPT: Precious 3 GPT model of Insilco Medicine; QC: Quantum Computing; RAG: Retrieval-Augmented Generation; RNN: Recurrent Neural Network.