Welocalize shares how utilizing NMT, NLP, and ML technologies within global healthcare provides patients with a more personalized experience.

Frost & Sullivan forecasts that 90% of U.S. hospitals will have adopted AI by 2025, improving positive outcomes by 30% to 40%. Accenture estimates AI can save the U.S. healthcare system as much as $150 billion in annual savings by 2026.

Chatbots are among the AI initiatives increasing in use. To ensure healthcare organizations understand the opportunities, challenges, and best practices of developing and deploying multilingual chatbots, Welocalize has published a complimentary guide, “Navigating the Future of Healthcare with Conversational AI.”

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Download the guide to learn:
– How AI powers the new generation of conversational chatbots,
– The use cases of conversational AI in healthcare and,
– The four steps to develop a multilingual chatbot.

Key takeaways in the guide include:
– Chatbots are not replacing doctors. However, they can take the load off healthcare professionals, including nurses and physician assistants, by supporting daily patient activities such as booking appointments, screening patients, and sending reminders to patients.
– The importance of training data. Chatbots use natural language processing (NLP) and machine learning (ML) to understand context, emotion, and user intent. However, a chatbot is only as good as the data it’s fed.
– Launching a multilingual chatbot for global healthcare and life sciences companies provides customers with a personalized experience. A multilingual chatbot is necessary, as people prefer to interact in their native language.

AI powers the new generation of conversational chatbots. If trained appropriately, they can even understand and respond in multiple languages. This allows healthcare providers and life sciences companies to engage in multilingual conversations across markets worldwide.

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