Clinical Report: Amendments to Confidence Assessment Metrics in MLLMs

Background

Confidence assessment in MLLMs is critical for their application in ultrasound radiology. The integrity of these models relies on precise metrics to evaluate their performance in complex medical environments. Recent advancements in MLLMs necessitate updates to ensure reliability and effectiveness in clinical practice.

Data Highlights

No numerical data is presented in the source material.

Key Findings

• Removal of a footnote from Table 2 in the original study.
• Correction of the Shannon entropy formula in the Confidence Measurement Metrics section.
• Revised values for “GPT-5” and “Majority vote (5), n (%)” in Table 1.
• The corrected article has been resubmitted to PubMed and other repositories.

Clinical Implications

Healthcare professionals should be aware of the importance of accurate confidence metrics in MLLMs to ensure reliable diagnostic support. Continuous updates and corrections to these metrics are vital for maintaining the integrity of AI applications in radiology.

Conclusion

The amendments to confidence assessment metrics in MLLMs highlight the need for precision in evaluating AI tools in ultrasound radiology.

Related Resources & Content

1. JMIR Medical Informatics, 2026 -- Confidence Measurement Metrics in MLLMs
2. npj Digital Medicine — Assessment of Large Language Models for Generating Diagnostic Impressions from Brain MRI Reports: A Multicenter Benchmark Study
3. Frontiers in Medicine — Benchmark evaluation of multi-modal large language models for ophthalmic diagnosis in real world
4. European Radiology — Simplifying radiology reports with large language models: privacy-compliant open- versus closed-weight models
5. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence
6. Assessment of Large Language Models for Generating Diagnostic Impressions from Brain MRI Reports
7. Benchmark evaluation of multi-modal large language models for ophthalmic diagnosis in real world
8. Simplifying radiology reports with large language models
9. Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study - PubMed
10. Confidence in radiology AI: From black-box scores to trusted decisions - ScienceDirect