The article presents a detailed analysis of evaluation metrics for individual subjects based on two different fine-tuning scenarios: 40 hours and 1 hour of data. The findings, encapsulated in Tables 7 and 8, reveal that models fine-tuned with 40 hours of data consistently outperform those fine-tuned with just 1 hour. This variability in performance metrics highlights the critical impact of data volume on model accuracy and robustness, emphasizing the necessity for extensive training data in medical AI applications.
Evaluations conducted on individual subjects utilizing fine-tuning data of 40-hours and 1-hour indicate significant variances in performance metrics, encompassing accuracy and robustness.
Key findings from Tables 7 and 8 highlight that comprehensive 40-hour fine-tuning yields superior evaluation metrics compared to the limited 1-hour data, underscoring the importance of data quantity.
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