People who have trouble hearing face many problems when they go to the doctor. The World Health Organization (WHO) says that over 430 million people worldwide have serious hearing loss. This number might grow to over 700 million by 2050. In the U.S., many deaf people use American Sign Language (ASL). ASL is very different from English, so this can cause problems during medical visits.
Studies show that many deaf patients find it hard to understand medical instructions. This can make them feel frustrated and confused, especially in emergencies where clear and quick communication is needed. Usual help like human interpreters or video remote interpretation (VRI) have problems too. Sometimes interpreters are not available. VRI needs a steady internet connection and can risk patient privacy because others might hear sensitive information.
Sign language recognition systems use artificial intelligence (AI) and human-computer interaction (HCI) to help deaf patients and healthcare providers communicate. These systems use cameras and sensors to capture hand signs and facial expressions that are part of sign language. AI programs, especially those using computer vision and neural networks, translate these signs into text or speech. Some systems can also change spoken or written language back into sign language, allowing two-way communication.
Teams creating these systems include doctors, computer experts, and language specialists. This mix helps make sure the systems understand sign language well and work properly in real healthcare settings. For example, researchers like Milena Soriano Marcolino in Brazil have worked to build tools that reduce the need for human interpreters, helping deaf users keep their privacy and independence.
Sign language recognition technology is tested in many healthcare places. These include regular doctor visits, online consultations, and emergency rooms. Each place has different needs. Emergency rooms need quick and clear communication, so the technology must be accurate and fast. Tests often use practice videos and real patient meetings to check how well the system works, how easy it is to use, and how much it helps communication.
In the U.S., hospitals with many types of patients can gain from these systems. But to work well, systems must handle different ASL dialects and fit into current healthcare computer systems. Good internet and user-friendly screens or tablets also make the systems easier to use and more accepted.
American Sign Language is the main sign language used by deaf people in the U.S. However, ASL is not the same everywhere. Different regions have their own variations. If AI systems are not trained on many versions of ASL, they might not understand all users correctly. This is a challenge because AI needs large and varied data to learn well.
Some deaf people use a mix of signs, gestures, and lip-reading. This makes translation even harder. Healthcare workers need to know that no single solution works for all cases. Training staff and having other communication options is important alongside technology.
One good thing about AI sign language recognition is that it can lower the need for human interpreters. This helps deaf patients keep their health information more private. It also lets patients talk to doctors directly without needing someone else to repeat.
But this also brings some problems like keeping data safe and making sure the system works well. Hospitals must follow privacy laws like HIPAA. They have to protect videos and translation data from being accessed by people who should not see them.
Using AI sign language recognition fits into a larger trend of automating tasks in healthcare. For hospital managers and IT teams in the U.S., these systems can make patient check-in and communication easier and faster.
To use sign language recognition in U.S. healthcare, the right hardware and software are needed:
Healthcare IT teams should think about these parts carefully. They need to make sure the new systems work well with electronic health records (EHRs) and telemedicine platforms already in use.
Although there has been progress, sign language recognition for healthcare in the U.S. is still new compared to other AI tools like facial or speech recognition. Current problems include:
Future work should create systems that can learn different dialects and recognize more types of communication like facial expressions and lip reading. It is important to get feedback from both deaf patients and healthcare providers to improve these tools.
Also, pilot tests and clinical trials in U.S. healthcare should be supported to prove these technologies work well in real life. Teamwork among healthcare administrators, AI experts, language specialists, and patient advocates is needed to bring these tools into everyday use.
Using AI-powered sign language recognition in healthcare can help reduce communication problems for deaf patients. This can make care safer and better. Health system leaders in the U.S. should learn about how these systems work and what is needed to use them. With the right setup and support, these tools can help make medical communication easier and more independent for deaf people. As research goes on, these systems will likely become an important part of care for the deaf community.
The review focuses on sign language recognition systems that utilize human-computer interaction and AI techniques to translate sign language into oral or written language, and vice versa, tested with human users in healthcare settings, ranging from primary care to emergency units, designed to improve communication between deaf patients and healthcare workers.
These technologies are used in various healthcare contexts including general clinical settings, emergency care, teleconsultations, and pre-attendance medical situations, aiming to facilitate timely communication and enhance patient outcomes, especially in acute and chronic care environments.
The systems primarily focus on translating dominant sign languages such as American Sign Language (ASL) and Brazilian Sign Language (Libras), alongside corresponding spoken or written oral languages. The diversity of sign language dialects presents generalizability challenges.
These systems typically require imaging hardware like cameras for gesture capture, AI frameworks including neural networks for gesture recognition, and software capable of language translation and human-computer interaction. Stable internet and compatible display devices enhance usability.
Development involves multidisciplinary teams—combining expertise in health, computing, AI, and linguistics—to design human-computer interaction interfaces. Systems are trained and tested using video data and human users, applying machine learning techniques such as computer vision and neural networks to recognize and translate signs.
Systems are tested both in simulated environments using video data and real-world healthcare encounters involving deaf users. Testing evaluates translation accuracy, usability, flexibility, and effectiveness in improving communication during healthcare interactions.
They enhance autonomy by reducing dependence on interpreters, improving privacy and inclusivity, and facilitating accurate transmission of medical instructions, thereby potentially decreasing preventable adverse events caused by communication barriers.
Efficacy is assessed through accuracy metrics of recognition, qualitative usability feedback from deaf users and healthcare professionals, communication effectiveness measures, and analysis of healthcare outcomes such as reduced miscommunication and improved patient satisfaction.
Some systems are bidirectional, capable of translating sign language to oral/written language and vice versa, enabling two-way communication between deaf patients and healthcare providers, although this capability varies and is an important criterion in the review.
Key gaps include limited focus on communication outcomes over technical innovation, challenges adapting to diverse sign language dialects, and underrepresentation of emergency care contexts. Future directions emphasize creating adaptive, scalable, inclusive systems accounting for dialects and user diversity, and integrating broader communication methods beyond sign language.
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