A Current Review of Generative AI in Medicine: Core Concepts, Applications, and Current Limitations
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Summary
This summary is machine-generated.Generative AI offers new tools for healthcare, creating synthetic data and aiding tasks like documentation and diagnostics. Understanding its capabilities and limitations is key for medical professionals in the evolving field.
Area Of Science
- Medical Informatics
- Artificial Intelligence in Healthcare
Background
- Generative Artificial Intelligence (AI) models create novel synthetic data, differing from discriminative models.
- Key families include diffusion models, Large Language Models (LLMs), and Large Multimodal Models (LMMs).
Purpose Of The Study
- Provide a foundational overview of Generative AI for non-engineering healthcare professionals.
- Clarify current capabilities, applications, and limitations of Generative AI in medicine.
Main Methods
- Review of Generative AI model families (diffusion, LLMs, LMMs).
- Analysis of current and specialized healthcare applications.
- Discussion of challenges including knowledge gaps, hallucinations, bias, and interpretability.
Main Results
- Generative AI applications include synthetic image generation, automated documentation, and training simulations.
- Specialized uses involve diagnostic aids, Retrieval-Augmented Generation (RAG), and multi-agent workflows.
- Potential benefits span imaging, documentation, education, and decision support.
Conclusions
- Generative AI has transformative potential in medicine but faces challenges like data bias and 'hallucinations'.
- Understanding these technologies is crucial for healthcare professionals, though AI is unlikely to replace physicians.
- Navigating regulatory and ethical issues is vital for successful integration.