Published: 2026-01-23 00:54

Evaluating Large Language Models for Brain MRI Diagnostic Impressions

The integration of artificial intelligence (AI) into healthcare continues to advance, with large language models (LLMs) emerging as a significant area of interest. These sophisticated models possess the ability to process and generate human-like text, offering potential applications across various medical disciplines.

In radiology, specifically for brain magnetic resonance imaging (MRI), the prospect of LLMs assisting in the generation of diagnostic impressions from complex findings is gaining attention. However, rigorous evaluation is paramount to ensure their accuracy, safety, and clinical utility.

The Role of Diagnostic Impressions in Radiology

Brain MRI reports are critical documents in patient care, providing detailed anatomical and pathological information. A key component of these reports is the diagnostic impression, also known as the conclusion.

This section summarises the radiologist’s interpretation of the imaging findings, highlighting the most significant observations, differential diagnoses, and often, recommendations for further action. The impression serves as a concise summary for referring clinicians, guiding subsequent treatment decisions and patient management.

The clarity, accuracy, and completeness of this impression are therefore crucial for effective clinical communication and patient outcomes.

Challenges and Opportunities for LLMs in Brain MRI Interpretation

Interpreting brain MRI scans and formulating a precise diagnostic impression is a complex task that requires extensive medical knowledge, pattern recognition skills, and clinical experience. Radiologists must synthesise numerous findings, consider patient history, and understand the clinical context.

This process can be time-consuming and demands a high level of cognitive effort.

LLMs present an opportunity to potentially streamline aspects of this workflow. By processing the descriptive findings section of an MRI report, an LLM could theoretically generate a preliminary diagnostic impression.

This could assist radiologists by providing a starting point for their own interpretation, potentially reducing report turnaround times or aiding in the identification of subtle findings. However, the inherent complexity of medical language, the nuances of imaging findings, and the critical need for absolute accuracy in diagnosis pose significant challenges for AI systems.

Methodology for Evaluating LLMs in Radiology

Given the high stakes in medical diagnosis, any application of LLMs in radiology must undergo thorough and systematic evaluation. A recent study published in npj Digital Medicine, titled “Evaluation of large language models for diagnostic impression generation from brain MRI report findings: a multicenter benchmark and reader study,” exemplifies the type of rigorous assessment required for these technologies (https://www.nature.com/articles/s41746-026-02380-4).

Such studies typically employ a multicenter approach, meaning data is collected and analysed from multiple institutions. This helps to ensure that the findings are generalisable and not specific to the practices or patient populations of a single centre.

A “benchmark study” involves comparing the performance of LLMs against established standards or other models. In this context, it often means assessing how well LLM-generated impressions align with those created by human expert radiologists.

A “reader study” is a common methodology in radiology research where multiple human readers (radiologists) independently interpret a set of images or reports. Their interpretations are then compared to a gold standard or to the outputs of an AI system.

This allows for a robust assessment of the AI’s performance relative to human experts, taking into account inter-reader variability.

The evaluation typically focuses on several key metrics:

• Accuracy: How often do the LLM-generated impressions correctly reflect the true diagnosis or the consensus of expert radiologists?
• Completeness: Do the impressions include all relevant findings and potential diagnoses?
• Conciseness: Are the impressions clear and to the point, without unnecessary verbosity?
• Clinical Relevance: Do the impressions prioritise clinically significant information?
• Safety: Do the LLMs avoid generating incorrect, misleading, or potentially harmful information?

Key Considerations for Clinical Integration

While the potential benefits of LLMs in radiology are considerable, their integration into clinical practice requires careful consideration. The current generation of LLMs, while powerful, can sometimes “hallucinate” or generate plausible-sounding but factually incorrect information.

In a diagnostic setting, such errors could have serious consequences for patient care.

Therefore, any LLM-generated diagnostic impression would likely serve as an assistive tool, requiring thorough review and validation by a qualified radiologist. The AI would function as a co-pilot, not an autonomous decision-maker. This human-in-the-loop approach is crucial for maintaining patient safety and accountability.

Furthermore, issues such as data privacy, algorithmic bias, and the explainability of LLM outputs need to be addressed. Ensuring that LLMs are trained on diverse and representative datasets is vital to prevent biases that could lead to disparities in care.

Understanding how an LLM arrives at a particular impression, rather than simply accepting its output, is also important for building trust and facilitating clinical oversight.

Limitations and Future Directions

Current evaluations of LLMs in radiology highlight both their promise and their limitations. While they may perform well on common or straightforward cases, their performance on rare conditions, complex multi-pathology cases, or subtle findings may vary.

The ability of LLMs to integrate non-imaging clinical data (e.g., laboratory results, patient history) into their impression generation is also an area of ongoing research and development.

Future research will likely focus on improving the robustness and reliability of these models, enhancing their ability to handle ambiguity, and developing methods for better explainability. Furthermore, studies will need to assess the real-world impact of LLMs on radiologist workflow, efficiency, and ultimately, patient outcomes in diverse clinical settings.

Conclusion

The evaluation of large language models for generating diagnostic impressions from brain MRI report findings represents a critical step in the responsible adoption of AI in healthcare. Studies employing multicenter benchmarks and reader studies are essential for understanding the capabilities and limitations of these technologies.

While LLMs hold significant potential to assist radiologists and enhance efficiency, their role is currently seen as a supportive tool, necessitating expert human oversight to ensure diagnostic accuracy and patient safety. Continued research and rigorous validation are vital to harness the benefits of AI while mitigating its risks in the complex field of medical imaging.

Key takeaways

• Large language models (LLMs) are being evaluated for their ability to generate diagnostic impressions from brain MRI report findings.
• Diagnostic impressions are crucial summaries in radiology reports, guiding clinical decisions.
• LLMs could potentially assist radiologists by streamlining impression generation, but face challenges due to the complexity and critical nature of medical diagnosis.
• Rigorous evaluation, often involving multicenter benchmark and reader studies, is essential to assess LLM accuracy, completeness, and safety.
• Any clinical application of LLMs in radiology will likely require human oversight and validation by qualified radiologists to ensure patient safety.
• Future research needs to address limitations such as potential errors, algorithmic bias, and the explainability of LLM outputs.

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Source: Nature