Medical transcription accuracy is crucial for safe and reliable clinical documentation, impacting patient safety and compliance. Traditional metrics like Word Error Rate (WER) and Character Error Rate (CER) are inadequate for medical settings because they treat all errors equally, failing to capture the clinical risk of inaccuracies, such as confusing medication dosages or omitting critical words. Errors like substitution, omission, insertion, and speaker attribution can lead to serious patient safety risks, legal challenges, and regulatory issues. To enhance transcription accuracy, combining AI's speed with human expertise in a hybrid model is recommended, where AI systems initially transcribe and medical professionals review for errors. Real-time accuracy monitoring, domain-specific model validation, and human-in-the-loop verification are strategies to improve outcomes, focusing on high-risk and low-confidence segments. This comprehensive approach addresses the challenges posed by medical vocabulary, environmental noise, and accented speech, ensuring that transcription systems maintain clinical accuracy in real-world scenarios.