Clinical Report: Correction of Grant Number in Breast Cancer LLM Study

Overview

This report addresses a correction in the grant number associated with a study evaluating large language models (LLMs) in assisting radiologists for breast cancer. The correction does not impact the study's findings or conclusions.

Background

The integration of large language models in radiology has the potential to enhance diagnostic accuracy and efficiency, particularly in complex cases such as breast cancer. Accurate funding attribution is crucial for maintaining the integrity of research and ensuring proper acknowledgment of support. This correction highlights the importance of precise documentation in clinical studies.

Data Highlights

No numerical data or trial results are affected by this correction.

Key Findings

• The grant number correction does not alter the scientific findings of the study.
• LLMs have shown promise in assisting radiologists within multidisciplinary teams.
• Accurate funding details are essential for research credibility.
• LLMs may improve diagnostic processes in breast cancer imaging.
• Future studies should continue to validate the role of LLMs in clinical settings.

Clinical Implications

Healthcare professionals should remain aware of the evolving role of LLMs in radiology, particularly in breast cancer diagnostics. Accurate funding and documentation are vital for the credibility of research findings and their application in clinical practice.

Conclusion

The correction of the grant number reinforces the importance of precise documentation in clinical research without affecting the study's conclusions. Continued exploration of LLMs in radiology is warranted.

References

1. JMIR Medical Informatics, 2026 -- Evaluation of Large Language Models for Radiologists’ Support in Multidisciplinary Breast Cancer Teams: Comparative Study
2. Int. Journal of Computer Assisted Radiology and Surgery (Springer) — Estimation of histopathological types from breast MRI findings using a large language model
3. npj Digital Medicine — Assessment of Large Language Models for Generating Diagnostic Impressions from Brain MRI Reports: A Multicenter Benchmark Study
4. European Radiology — Differentiating Intra-Axial Primary Brain Tumors: A Comparison of Large Language Models Analyzing Structured MRI Reports Versus Radiologists Interpreting Images
5. the asco post — Large Language Models May Generate Concise, Coherent Pathology Summaries, Reducing Physician Burden
6. ACR Appropriateness Criteria Update 2025
7. Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial - ScienceDirect
8. JMIR Medical Informatics - Evaluation of Large Language Models for Radiologists’ Support in Multidisciplinary Breast Cancer Teams: Comparative Study