Generative Artificial Intellegence (AI) in Pathology and Medicine: A Deeper Dive DOI Creative Commons
Hooman H. Rashidi, Joshua Pantanowitz, Alireza Chamanzar

и другие.

Modern Pathology, Год журнала: 2024, Номер unknown, С. 100687 - 100687

Опубликована: Дек. 1, 2024

This review article builds upon the introductory piece in our seven-part series, delving deeper into transformative potential of generative artificial intelligence (Gen AI) pathology and medicine. The explores applications Gen AI models medicine, including use custom chatbots for diagnostic report generation, synthetic image synthesis training new models, dataset augmentation, hypothetical scenario generation educational purposes, multimodal along with multi-agent models. also provides an overview common categories within discussing open-source closed-source as well specific examples popular such GPT-4, Llama, Mistral, DALL-E, Stable Diffusion their associated frameworks (e.g. transformers, GANs, diffusion-based neural networks), limitations challenges, especially medical domain. We libraries, tools that are currently deemed necessary to build integrate Finally, we look future, impact on healthcare, benefits, concerns related privacy, bias, ethics, API costs, security measures.

Язык: Английский

Generative Artificial Intellegence (AI) in Pathology and Medicine: A Deeper Dive DOI Creative Commons
Hooman H. Rashidi, Joshua Pantanowitz, Alireza Chamanzar

и другие.

Modern Pathology, Год журнала: 2024, Номер unknown, С. 100687 - 100687

Опубликована: Дек. 1, 2024

This review article builds upon the introductory piece in our seven-part series, delving deeper into transformative potential of generative artificial intelligence (Gen AI) pathology and medicine. The explores applications Gen AI models medicine, including use custom chatbots for diagnostic report generation, synthetic image synthesis training new models, dataset augmentation, hypothetical scenario generation educational purposes, multimodal along with multi-agent models. also provides an overview common categories within discussing open-source closed-source as well specific examples popular such GPT-4, Llama, Mistral, DALL-E, Stable Diffusion their associated frameworks (e.g. transformers, GANs, diffusion-based neural networks), limitations challenges, especially medical domain. We libraries, tools that are currently deemed necessary to build integrate Finally, we look future, impact on healthcare, benefits, concerns related privacy, bias, ethics, API costs, security measures.

Язык: Английский

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