Artificial Intelligence is being hailed as a potential game-changer in the healthcare language solutions industry, promising unprecedented access to translation and interpreting services across all touchpoints in a patient’s health journey. For limited English proficiency (LEP) patients and their providers, the benefits are potentially life-changing, ranging from greater accuracy, speed and convenience to lifesaving interventions that positively impact outcomes, safety, treatment adherence and more.
Understandably, healthcare leaders are excited to integrate AI interpretation across the care continuum, vastly improving the healthcare experience for America’s increasingly diverse and geographically dispersed population of LEP patients. However, given the flurry of vendors entering the AI linguistics market today, knowing where to start and who to trust may seem overwhelming.
GLOBO research report evaluates AI technologies for provider-patient communication
To help providers evaluate AI capabilities, GLOBO conducted a three-month research study in 2024 to evaluate the performance of multiple large language models (LLMs) and their multimodal variants. We found that all AI technologies are not created equal. Our findings and observations are presented in our newly published research, The GLOBO AI-Powered Medical Interpretation Study: Insights for Health Leaders, which is available for download here.
Our research is designed to provide healthcare leaders with key insights into AI interpretation performance. This guide presents an executive summary focused on four key domains:
1. Assessing the process of AI interpretation
2. Evaluating how AI-enabled interpretation is measured
3. Exploring the current state of AI tools
4. Identifying where LLMs fall short with interpretation
Unlike human interpreters, AI requires a series of steps to successfully communicate from one language to another:
1. The verbal message is transcribed into text
2. Text is translated into the selected language
3. The message is converted into speech
While this may seem relatively straightforward, each step requires a specific AI technology to perform the transcription, translation and speech functions. When integrated, they must fulfill the same role as a human interpreter, meeting stringent quality measures for:
- Accuracy – Conveying messages between both parties, completely and with all components, including tone, register and cultural context
- Realism – Accurately interpreting the clinical situation, including expressing empathy in emotionally charged situations
- Latency – Reducing the amount of time it takes to transcribe the spoken word, translate it into the new language, and speak it back in the moment to ensure that patients understand
- Cost – Like all technology, you get what you pay for. The more accurate and realistic your AI technology is, the more it will cost.
With no out-of-the-box solutions optimized specifically for medical interpreting, healthcare organizations must be prepared to configure and fine-tune AI models to ensure they meet all predefined quality measures, which ultimately will increase efficiency and reduce costs.
When assessing the limits of available LLMs, we identified common pitfalls during each step of the AI interpretation process. In the transcription phase, some models simply couldn’t handle transcribing multiple languages at a time. They also had difficulty transcribing short statements and were essentially incapable of assessing uncertainty, lacking a common-sense filter to ask for clarity, which a live interpreter would instinctively do.
For translation, we found that LLMs may refuse to translate critical information, mistakenly flagging a text as harmful or inappropriate. Worse, LLMs may hallucinate, duplicate or completely leave out details of a translated text, and incorrectly translate a word based on the context of the conversation.
Speech synthesis models also produced hallucinated speech, repeating syllables or unintentionally creating audio artifacts such as yawns. Speech LLMs had difficulty with the pronunciation of short statements and generated non-native-sounding speech in some cases.
All these challenges aside, it is important to acknowledge that AI tools are evolving rapidly, with the current language models continuously learning and being fine-tuned to create better outputs and user experiences. For instance, OpenAI, which launched its first version of ChatGPT in 2018, released its fourth generation in 2023, building on previous releases to enhance its output and ability to generate more advanced responses. As OpenAI and other technology companies continue to expand language models, tasks once deemed impossible are now becoming a reality.
It is incumbent on healthcare leaders to keep pace – or get left behind.
To effectively integrate AI interpretation into care settings, healthcare leaders must focus on finding a solution that can deliver fast, accurate and empathetic interpretation. Collaborating with a trusted partner is one way to ensure AI technologies are tested and configured to meet your organization’s language interpretation needs at all points of care interaction across the patient’s health journey.
Since 2010, GLOBO has been a leader in interpreting and translation services, providing an innovative approach to communicating with diverse patient populations. Our team of experts is dedicated to testing and designing the right AI-enabled tools to help your hospital, health system or medical practice communicate with multilingual patients when it matters most.
Don’t allow the complexities of AI to hinder your goals for enhancing and expanding linguistic services to your healthcare staff and patients. Download The GLOBO AI-Powered Medical Interpretation Study: Insights for Health Leaders, and let’s discuss how we can help your organization leverage AI interpretation tools to better serve your non-English-speaking population.
Dipak Patel is CEO of GLOBO Language Solutions, a B2B provider of translation, interpretation and technology services for multiple industries. Prior to GLOBO, Patel spent 20-plus years in corporate healthcare leadership roles. The son of immigrants, he understands the significance of eliminating language barriers to improve healthcare equity.
