⚡ Quick Summary

This study evaluated the confidence levels of 12 large language models (LLMs) in answering clinical questions across five medical specialties. The findings revealed a significant inverse correlation between model accuracy and confidence, highlighting potential challenges in their clinical application.

🔍 Key Details

• 📊 Dataset: 1965 multiple-choice questions
• 🧩 Medical specialties: Internal medicine, obstetrics and gynecology, psychiatry, pediatrics, general surgery
• ⚙️ Models evaluated: 12 LLMs, including GPT-4o and Qwen2-7B
• 🏆 Key metrics: Mean accuracy and confidence scores

🔑 Key Takeaways

• 📉 Inverse correlation found between confidence scores and model accuracy (r=-0.40; P=.001).
• 🏅 Top-performing model GPT-4o achieved a mean accuracy of 74% with a confidence of 63%.
• 📉 Low-performing model Qwen2-7B had a mean accuracy of 46% but a confidence of 76%.
• 🔍 Minimal variation in confidence between correct and incorrect answers (0.6% to 5.4%).
• 💡 Highest mean difference in confidence for GPT-4o at 5.4% (SD 2.3%; P=.003).
• ⚠️ Overconfidence in lower-performing models raises concerns for clinical use.
• 🔄 Recommendations include refining calibration methods and involving human oversight.
• 🔬 Further research is essential for improving LLMs before clinical adoption.

📚 Background

The use of large language models in the biomedical field is rapidly evolving, yet their ability to accurately assess their own confidence in clinical scenarios remains largely unexamined. Understanding how these models gauge their responses is crucial for ensuring their safe and effective integration into healthcare settings.

🗒️ Study

This cross-sectional evaluation study aimed to benchmark the confidence of various LLMs in answering clinical questions. Researchers utilized a comprehensive dataset of 1965 multiple-choice questions spanning five medical specialties, prompting models to provide both answers and confidence scores ranging from 0% to 100%.

📈 Results

The analysis revealed a statistically significant inverse correlation between the mean confidence scores for correct answers and the overall accuracy of the models. For example, while GPT-4o demonstrated a commendable mean accuracy of 74%, it maintained a mean confidence of only 63%. In contrast, the less accurate Qwen2-7B exhibited a higher confidence level of 76%, despite its lower accuracy of 46%.

🌍 Impact and Implications

The findings of this study underscore the importance of aligning confidence levels with actual performance in LLMs. The tendency for lower-performing models to exhibit higher confidence could pose risks in clinical decision-making. Addressing these discrepancies through improved calibration and oversight is vital for the safe deployment of LLMs in healthcare environments.

🔮 Conclusion

This study highlights the critical need for further research into the confidence assessment capabilities of LLMs in clinical contexts. While promising, the current findings indicate that even the most accurate models show limited variation in confidence between correct and incorrect answers. Enhancing these models through targeted strategies will be essential for their broader clinical adoption.

💬 Your comments

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