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Generative Artificial Intelligence in Pathology and Medicine: A Deeper Dive.

Hooman H Rashidi1, Joshua Pantanowitz2, Alireza Chamanzar1

  • 1Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.

Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc
|December 17, 2024
PubMed
Summary

Generative AI (Gen AI) offers transformative potential in pathology and medicine, with applications in diagnostics, training, and education. This review explores Gen AI models, tools, and future healthcare impacts, including benefits and challenges.

Keywords:
ChatGPTdiffusiongenerative adversarial networkgenerative artificial intelligencegenerative pretrained transformermultiagent

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Computational Pathology

Background:

  • Generative Artificial Intelligence (Gen AI) is rapidly evolving, presenting significant opportunities for integration into medical practices.
  • Pathology and medicine are exploring novel AI applications to enhance diagnostic accuracy, research, and education.

Purpose of the Study:

  • To provide a comprehensive review of Generative AI applications in pathology and medicine.
  • To explore various Gen AI models, frameworks, and their limitations within the medical domain.
  • To discuss the future impact, benefits, and challenges of Gen AI in healthcare.

Main Methods:

  • Review of current literature and popular Gen AI models (GPT-4, Llama, DALL-E, Stable Diffusion).
  • Exploration of Gen AI applications: chatbots, synthetic image generation, data augmentation, and educational tools.
  • Analysis of Gen AI frameworks (transformers, GANs, diffusion models) and necessary libraries/tools.
  • Discussion of ethical, privacy, security, and cost considerations.

Main Results:

  • Gen AI models demonstrate diverse applications including diagnostic report generation, synthetic data creation, and enhanced medical education.
  • Popular Gen AI models and frameworks have been identified, alongside their specific limitations in medical contexts.
  • Essential tools and libraries for Gen AI implementation in healthcare have been reviewed.

Conclusions:

  • Gen AI holds substantial promise for revolutionizing pathology and medicine, offering improved diagnostics and personalized patient care.
  • Addressing challenges related to privacy, bias, ethics, and security is crucial for successful Gen AI integration in healthcare.
  • The future of healthcare will likely involve sophisticated Gen AI tools, necessitating careful development and deployment strategies.