Introduction

In the dynamic and sensitive world of healthcare, accuracy in communication is critically imperative to providing safe, equitable and exceptional quality of patient care. Miscommunication between clinicians and patients, particularly those who speak different languages, can lead to critical misdiagnoses, delays in treatment and compromised patient safety, that may be the difference between life or death in certain cases. As the field of technology rapidly evolves, so too do novel opportunities that foster enhanced quality and accessibility of communication between Limited-English Speaking Patients (LEP) and their care teams. Aavaaz is deeply dedicated to solving this devastating unmet need with the next generation of state-of-the-art multilingual, multimodal translation technologies. To ensure our technology meets the highest standards of reliability and precision, we have benchmarked our model performance against industry-grade machine translators and conducted rigorous evaluations tailored to the unique and vast demands of clinical care settings. This report provides a detailed analysis of our superior accuracy metrics and highlights their critical relevance to ensuring reliable, safe, and precise machine translation in healthcare.

Benchmarking Metrics & Methodology

To deeply ensure the robust accuracy of Aavaaz’s neural machine translation models, we utilized a comprehensive set of both industry-standard metrics and internal Aavaaz validation techniques.

Industry Standards

1. BLEU (Bilingual Evaluation Understudy Score): Assesses the closeness of our translations to human professionally translated reference texts.
2. METEOR (Metric for Evaluation of Translation with Explicit ORdering): Focuses on linguistic precision and context, including synonyms, stemming, and word order.
3. Human Feedback: Validates translations for fluency, contextual relevance, and clinical appropriateness via panel of expert linguists, medical interpreters, and clinicians evaluated

Aavaaz Internal Validation Techniques:

1. Modular Agentic System: Enables precise and deep error detection, providing a reliable framework for producing accurate and contextually sound translations. The multi-agent system archiecture includes two core arms:
   1. Dedicated agent for identifying grammatical, lexical, and contextual translation errors
   2. Specialized in evaluating the colloquiality of translations specific to each language linguistic pattern and culture norms, ensuring outputs align with natural and culturally appropriate language
2. Deep Expert-Led Validation: Enhances review beyond baseline translations uncovering deep cultural, colloquial and conversational elements critical for healthcare communication.

After multiple iterations of model training, human feedback, iteration and retraining were completed demonstrating strong consistency of acceptable outputs, the final model metrics were conducted as below using BLEU and METEOR scores.

To capture a broad range of real-world situations and ensure generalizability of results, phase 1 testing language was analyzed on a set of 6 common clinical conversation types across 10 specialties as listed below:

Conversation Types:

1. Initial Patient Visit
2. Treatment Discussion
3. Medical Team Rounds
4. Medication Reconciliation
5. Patient Education
6. Discharge Instructions

Medical Specialties:

1. Emergency Medicine
2. Cardiology
3. Oncology
4. General Inpatient Medicine
5. Ambulatory Care
6. Allergy & Immunology
7. Ophthalmology
8. Endocrinology
9. Physical Medicine
10. Surgery

Base Reference Testing Dataset

The base dataset was compiled through human translated text, including a variety of inputs from medical interpreters, multilingual clinicians, linguists, and community members to avoid any potential bias from singular translations by one individual. The robust testing dataset includes bidirectional patient and doctor conversations across six conversation types and spanned ten medical specialties (as listed above). These initial conversation and specialty categories were chosen based on the highest relevance of clinical scenarios – further expansion will continue to represent broader context across healthcare experiences. The base dataset will also be released for public view in upcoming publications.

Metrics Visualization & Analysis:

This analysis evaluated the superior performance of Aavaaz, a neural machine translation model, as compared to Google Translate and GPT across multiple languages, utilizing BLEU and METEOR scores to assess translation quality, accuracy and reliability. BLEU and METEOR were selected considering wide standard usage in assessing the quality of machine-generated translations by comparing them to human references. The plots included below provide graphical illustrations to visualize average scores for each system, highlighting relative performance across different languages. This analysis is relevant for understanding the strengths and weaknesses of these systems in multilingual translation tasks, particularly in the context of sensitive clinical conversations between patients and care teams.

Comparative Results Analysis:

1. BLEU Scores
   • The BLEU scores plot displays average BLEU scores for Aavaaz, Google, and GPT across the phase 1 languages currently supported by Aavaaz. BLEU scores range from 0 to 1, with higher scores indicating better translation quality. The plot reveals that Aavaaz dominates in several languages, achieving consistently higher BLEU scores compared to incumbents such as Google and GPT. This dominance reinforces the Aavaaz excellence in maintaining precision and word-order accuracy, particularly in languages with complex grammatical structures. In medical conversations, where high precision is critical—such as when translating symptoms, diagnoses, sentiments of patients or treatment instructions from care teams —even the slightest errors can lead to severe and potentially fatal misunderstandings. Aavaaz outcompeting both Google and GPT in average BLEU scores of all languages, not only displays clear comparative superiority but also highlights the robust ability to deliver accurate and reliable translations with almost near perfection in comparison to human reference data collected from medical interpreters, multilingual clinicians and community members.


2. METEOR Scores
   • The second plot illustrates the average METEOR scores for the three models across the same set of languages. METEOR scores also range from 0 to 1, with higher scores reflecting better alignment with human reference datasets. Within this plot as well, Aavaaz outperforms Google and GPT in most languages, with consistently higher METEOR scores, indicating not only high precision but also superior performance in capturing contextual elements of conversation such as key synonyms, word order and paraphrasing, which are crucial for natural-sounding translations. Aavaaz’s superiority METEOR score indicates higher ability to understand word relations and overall contextual relevance, both key when conducting sensitive patient care discussions. In clinical conversations, where the ability to capture synonyms and paraphrasing is particularly important—such as when patients and healthcare providers use informal or varied language to describe symptoms or conditions—Aavaaz's strong performance in METEOR scores suggests it can handle such variability effectively and precisely.

Conclusion

The evaluation of BLEU and METEOR scores across multiple languages highlights Aavaaz's exceptional performance, consistently surpassing Google and GPT models in most cases with significantly higher average scores. These results reflect Aavaaz's unparalleled superiority against leading incumbent technologies to not only accurately translate at baseline, but to also handle complex linguistic structures and cultural scenarios that to deliver precise, contextually accurate translations, positioning it as the gold standard for healthcare-related colloquial translations. The superior accuracy and precision of Aavaaz emphasizes its critical role in ensuring reliable and clear communication across a broad range of clinical settings, where even minor errors can have serious implications. This report not only showcases Aavaaz’s leadership, strength and reliability in multilingual translation technologies but also reinforces its future potential to redefine industry standards, ensuring safe, robust, and dependable solutions that redefine health equity across the global borders.