Generative Artificial Intelligence in Pathology and Medicine: A Deeper Dive
A team of researchers led by H.H. Rashidi has published a comprehensive review in Modern Pathology detailing the transformative potential of generative artificial intelligence in healthcare. This work serves as the second installment in a seven-part series dedicated to exploring how generative models are reshaping pathology and medicine. The article explores practical applications including custom chatbots for diagnostic report generation, synthetic image synthesis for training new models, and dataset augmentation. It also discusses the use of multimodal and multiagent models for educational purposes and hypothetical scenario generation. Specific examples of popular models covered include GPT-4, Llama, Mistral, DALL-E, and Stable Diffusion, alongside their associated frameworks such as transformers and diffusion-based neural networks. Beyond technical capabilities, the review addresses common libraries and tools necessary for building and integrating these models. The authors conclude by discussing the future impact on health care, weighing benefits against challenges related to privacy, bias, ethics, application programming interface costs, and security measures. The study was assigned PMID 39689760 and is available via the Modern Pathology journal.
公開日: June 18, 2026 at 10:00 AM
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A team of researchers led by H.H. Rashidi has published a comprehensive review in Modern Pathology detailing the transformative potential of generative artificial intelligence in healthcare. This work serves as the second installment in a seven-part series dedicated to exploring how generative models are reshaping pathology and medicine.
The article explores practical applications including custom chatbots for diagnostic report generation, synthetic image synthesis for training new models, and dataset augmentation. It also discusses the use of multimodal and multiagent models for educational purposes and hypothetical scenario generation. Specific examples of popular models covered include GPT-4, Llama, Mistral, DALL-E, and Stable Diffusion, alongside their associated frameworks such as transformers and diffusion-based neural networks.
Beyond technical capabilities, the review addresses common libraries and tools necessary for building and integrating these models. The authors conclude by discussing the future impact on health care, weighing benefits against challenges related to privacy, bias, ethics, application programming interface costs, and security measures. The study was assigned PMID 39689760 and is available via the Modern Pathology journal.