Generative Artificial Intellegence (AI) in Pathology and Medicine: A Deeper Dive.

This review article builds upon the introductory piece in our seven-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, dataset augmentation, hypothetical scenario generation for educational purposes, and the use of multimodal along with multi-agent models. This article also provides an overview of the common categories within generative 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 (e.g. transformers, GANs, 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 generative AI on healthcare, including benefits, challenges, and concerns related to privacy, bias, ethics, API costs, and security measures.