When "Wrong" Looks “Right”: The Challenge of Evaluating AI in Healthcare
Choosing the right evaluation metrics is crucial for ensuring patient safety and clinical accuracy when integrating AI into healthcare. Traditional text comparison metrics like F1, BLEU, ROUGE, and METEOR often fail to distinguish between clinically accurate and inaccurate responses. Advanced methods such as BERTScore, ClinicalBERT, and MoverScore, show better results but still have limitations. In this blogpost, we present a compelling case for investing in more advanced evaluation methods, even when they require additional computational resources. When patient safety is at stake, the ability to reliably distinguish between clinically accurate and inaccurate content isn't just nice to have—it's essential.
