Evaluation of Bidirectional Sign Language Translation Technologies and Their Impact on Enhancing Patient Autonomy and Privacy in Clinical Settings

Deaf patients in the United States often have trouble when they visit doctors or hospitals. It can be hard for healthcare workers and deaf patients to understand each other. This can cause mistakes in diagnosis or treatment. Worldwide, more than 430 million people have serious hearing loss. By 2050, this number might grow to over 700 million. So, fixing communication problems in medical places is important. In the U.S., patient independence and privacy are key parts of good care. New AI (artificial intelligence) tools may help improve talking between deaf patients and medical staff.

What Are Bidirectional Sign Language Translation Technologies?

Bidirectional sign language translation systems use AI tools like computer vision, neural networks, and machine learning. They can read hand signs and facial expressions in sign language. These systems turn sign language into spoken or written words that doctors can understand. They also change spoken or written words back into sign language for patients. American Sign Language (ASL) is the main sign language in the U.S. and is the focus of these technologies.

These systems need good cameras to see hand signs clearly. They also need software that can quickly process lots of visual information. These systems must handle different ASL dialects while staying accurate and fast. To test them, developers use video data and live trials with deaf users.

Healthcare Settings and Patient Independence

Usually, healthcare workers use human interpreters or video remote interpreting (VRI) services to talk with deaf patients. But these ways can have problems. It can be hard to schedule interpreters, and they may not always be available. Also, these services can be expensive and raise privacy issues. Deaf patients sometimes feel uncomfortable sharing private health information through others. This can stop them from giving full details about their health.

Using AI-based bidirectional sign language translation can help patients talk directly with their doctors. This reduces the need for interpreters and protects patient privacy. This is very important in settings where privacy matters, like mental health or sexual health care.

Better communication also helps patients understand their health instructions better. Research shows deaf people often misunderstand health advice, which can lead to problems with self-care. These translation tools can give clear, fast translation both ways. That can reduce mistakes and bad health events.

Creating These Technologies: Teamwork Across Fields

Making good bidirectional sign language translation systems needs many experts to work together. This includes doctors, AI researchers, language specialists, and software engineers. For example, a group in Brazil at the Universidade Federal de Minas Gerais is studying these systems for medical use. They include deaf researchers to make sure the technology meets real user needs.

These systems use neural networks and natural language processing to correctly translate sign language into medical terms. They also need to work with electronic health records so they fit into normal hospital workflows. This kind of teamwork is important for making practical and easy-to-use systems.

Privacy and Safety Issues

Protecting patient privacy is very important and is required by U.S. laws like HIPAA. Traditional video remote interpreting can raise worries about data security during remote transmission. AI translation systems can solve some problems by doing translation on-site without sending video outside the healthcare place.

It is still necessary to keep data safe within these AI tools. They must use encryption, hide patient data, and control who can use the system. Medical centers should check that these tools follow privacy rules before using them.

Testing safety is also key, because wrong translations can cause serious harm to patients. These AI models need lots of testing with real people to check accuracy and speed. If a tool isn’t good enough, it should not be used.

Where These Technologies Are Used in U.S. Healthcare

Bidirectional sign language translation can help in many healthcare places, such as:

• Primary care clinics, for regular visits and managing chronic illnesses.
• Emergency rooms, where fast communication is needed and interpreters may not be there.
• Mental health services, improving communication in counseling and therapy.
• Telemedicine, helping remote visits without needing interpreters.
• Pre-appointment check-ins, speeding up registration and patient intake.

Although some places use these tools in outpatient or telehealth care, emergency use is difficult because fast detection of signs and changing conditions like low light make it hard. Research is ongoing to improve these systems for emergency rooms.

AI Help with Clinic Workflow

For hospital leaders and IT staff, adding AI sign language translation can improve office work as well as patient communication. AI can automate tasks like scheduling and phone answering. This lets staff focus more on patients.

For example, Simbo AI provides AI tools for phone automation. These tools handle bookings, answer questions, and guide calls. They can also work for deaf callers by linking to sign language translation.

Inside clinics, these translation systems can connect with electronic health records (EHR) and patient software. This helps keep translated information accurate and available to doctors and nurses. The system can also send reminders and alerts for follow-up care, specially designed for deaf patients.

Automating these routine tasks helps make sure privacy rules are followed and reduces mistakes. This improves efficiency, which is helpful in busy clinics with many different patients.

Problems and the Road Ahead

Even though these tools look promising, they still have challenges:

• Sign language varies by region and culture. Most systems focus on main versions like ASL but do not cover all variations, which limits access.
• These systems need good hardware, steady internet, and quality screens. Some clinics may not have enough technology for this.
• Many translation tools have limited testing in real healthcare settings, especially in emergency care.
• As AI becomes more independent, there need to be clear rules made by doctors, ethicists, and regulators about how to use these systems responsibly.

Researchers are trying to build systems that can adjust to different sign dialects and fast-paced clinical environments. Using AI that understands facial expressions and gestures will improve accuracy and make conversations feel more natural. Also, spreading these tools to clinics with fewer resources could help more deaf patients get fair health care.

Effects on Patient Care and Healthcare Services

Using bidirectional sign language translation can improve patient care by making communication more accurate and direct between deaf patients and doctors. It lowers the need for human interpreters, which can protect privacy and make care faster and sometimes cheaper.

Healthcare groups also meet legal requirements like the Americans with Disabilities Act (ADA) when they use these tools. This shows they want to provide accessible and patient-focused care.

Better communication means fewer medical mistakes caused by misunderstanding. This may reduce legal risks and make patients more satisfied with their care. It may also help keep patients coming back.

Summary

AI-driven bidirectional sign language translation is a helpful tool for improving communication with deaf patients in U.S. healthcare. It supports patient independence and privacy, makes office work smoother, and can lead to better health results. Clinic managers, facility owners, and IT staff should think about using these tools to offer more inclusive and effective healthcare services.