Does GPT4 dream of counting electric nodules?

Category	Detail
Condition	Application of large language models (LLMs) and vision-language models (VLMs) in radiology
Key Mechanisms	Generative AI models predict sequential tokens to generate text; multimodal inputs enable image and text processing; alignment via human feedback improves output quality
Target Population	Radiologists, medical researchers, clinicians, and academic radiologists
Care Setting	Clinical radiology practice, academic research, and medical data management

ChatGPT and GPT-4 can assist in summarizing radiology reports, data extraction, and de-identification tasks but are prone to hallucinations and errors without supervision.
Multimodal models enable integration of image and text data, facilitating advanced radiology applications such as image description and synthetic data generation.
Alignment processes and human feedback are critical to improving model reliability for clinical use, though regulatory approval remains a significant hurdle.

Do not rely solely on LLM outputs for diagnostic decisions due to potential inaccuracies and hallucinations.
Use LLMs as adjunct tools to assist in report summarization and data extraction under expert supervision.

Incorporate LLMs for handling unstructured data, formatting, translation, and coding support in academic radiology workflows.
Employ multimodal AI models to enhance image interpretation and training data generation.

Continuously validate AI-generated outputs against expert review to detect and correct errors.
Monitor updates in AI capabilities and regulatory guidelines to ensure safe clinical integration.

Risk of confident but incorrect information (‘hallucinations’) and fabricated references from LLMs.
Privacy concerns when processing large patient datasets; ensure proper de-identification.
Potential overreliance on AI outputs without adequate human oversight.

Not applicable; focus on radiology professionals and researchers using AI tools.

AI tools can streamline data handling and reporting but require expert validation to ensure clinical accuracy.

Use LLMs and VLMs as supportive tools rather than primary diagnostic sources.
Apply human-in-the-loop approaches to verify AI-generated content before clinical application.
Leverage AI for data extraction, de-identification, translation, and coding to improve workflow efficiency.
Stay informed on AI model updates, capabilities, and regulatory status to guide safe implementation.

Clinical Scorecard: Can GPT-4 Conceptualize the Quantification of Electric Nodules?