AI in Neuroradiology From Theory to Daily Practice
Artificial intelligence tools are making a tangible impact on the daily practice of neuroradiology, bringing significant changes to clinical workflows, diagnostic processes, and overall patient care. According to leading experts, a thorough understanding of both the capabilities and the limitations of these AI tools is crucial for their safe, effective, and responsible integration into the medical field.
“AI has the potential to impact basically every stage in the radiology imaging cycle,” notes Jason Talbott, MD, PhD, a professor at the University of California, San Francisco. “As these technologies continue to integrate into our workflow, it’s critical that we stay informed and prepared to evaluate and adopt them responsibly.”
AI is increasingly being applied in neuroradiology to assess acute neurological conditions, boost the efficiency of neuroradiologists, and speed up brain and spine imaging.
“We are at the frontier of AI in medicine, where its integration into clinical practice is no longer a future prospect but an evolving reality,” says Amish Doshi, MD, a professor and division chief of neuroradiology at the Icahn School of Medicine at Mount Sinai. “As AI continues to gain traction, radiologists are discovering its potential as a powerful tool to enhance patient care.”
Automating Reports with Large Language Models
One of the most significant applications of AI in radiology involves using large language models (LLMs) to streamline the generation of reports. Dr. Talbott explained that these advanced tools, which can produce human-like text, have the potential to automate, at least in part, the creation of neuroradiology reports.
He highlighted a recent study in Radiology where researchers successfully used GPT-4 to convert unstructured, free-text radiology reports into standardized, structured templates.
“GPT-4 was very effective for post hoc, standardized, structured report generation,” Dr. Talbott commented. “It was highly scalable, and there’s potential for structuring vast amounts of radiology data into more structured formats.”
However, this technology comes with challenges. The use of GPT-4 raises potential concerns about patient data privacy. Dr. Talbott also warned of the risk of “hallucinations,” where LLMs confidently generate false information. Other obstacles include inherent biases in the training data and difficulties integrating these models with existing radiology information systems.
The Frontier of AI in Brain Tumor Imaging
While AI's potential in neuroradiology is vast, its actual adoption in specialized fields like brain tumor imaging is still in its early stages.
“Despite thousands of studies on AI in neuroradiology, only about 126 FDA-cleared products exist, and few of them relate to brain tumor imaging,” stated Mariam S. Aboian, MD, PhD, an attending radiologist at Children’s Hospital of Philadelphia.
Dr. Aboian pointed to volumetrics—the process of measuring a tumor's total volume and tracking its changes over time—as a particularly promising area for AI tools. She also stressed the importance of ensuring that any new AI tool can integrate smoothly with the workflows of other medical specialties.
Key Challenges Generalizability and Model Evaluation
Another critical factor for successful AI implementation is generalizability. An AI model's performance can vary significantly outside of the environment where it was developed.
“If you develop an AI that works in your hospital and maybe three other hospitals through an external validation data set, does it mean it's going to work in the entire U.S.?” Dr. Aboian questioned. “Does it mean it's going to work in the entire world? Most likely not. So, you really want to evaluate the model generalizability.”
To properly assess an AI model's accuracy, Dr. Aboian recommended using specific metrics like the Dice coefficient and Hausdorff distance. She also urged radiologists to carefully consider how an algorithm will affect their workflow efficiency and influence critical medical decision-making.
