Generative Artificial Intelligence in Pathology and Medicine: A Deeper Dive
Hooman H. Rashidi, Joshua Pantanowitz, Alireza Chamanzar, Brandon D. Fennell, Yanshan Wang, Rama R. Gullapalli, Ahmad P. Tafti, Mustafa Deebajah, Samer Albahra, Eric F. Glassy, Matthew G. Hanna, Liron Pantanowitz
Abstract
This review article builds upon the introductory piece in our 7-part series, delving deeper into the transformative potential of generative artificial intelligence (Gen AI) in pathology and medicine. The article explores the applications of Gen AI models in pathology and medicine, including the use of custom chatbots for diagnostic report generation, synthetic image synthesis for training new models, data set augmentation, hypothetical scenario generation for educational purposes, and the use of multimodal along with multiagent models. This article also provides an overview of the common categories within Gen AI models, discussing open-source and closed-source models, as well as specific examples of popular models such as GPT-4, Llama, Mistral, DALL-E, Stable Diffusion, and their associated frameworks (eg, transformers, generative adversarial networks, diffusion-based neural networks), along with their limitations and challenges, especially within the medical domain. We also review common libraries and tools that are currently deemed necessary to build and integrate such models. Finally, we look to the future, discussing the potential impact of Gen AI on health care, including benefits, challenges, and concerns related to privacy, bias, ethics, application programming interface costs, and security measures.