AI-Powered Biometrics in Healthcare

Summary:

AI-powered biometrics technology has a great deal of promise in supporting the healthcare industry in its mission to protect patient privacy and identify patients with higher accuracy. With new tech innovations emerging, AI biometric systems can be helpful for medical diagnosing and COVID-19 restrictions adherence. 

Biometrics play a critical role in the healthcare industry. With higher demand for more efficient and secure solutions to safeguarding protected patient information, biometrics has stepped into the forefront. Experts say that the healthcare biometrics market will reach a value of $5.6 billion USD by 2022. 

Healthcare technology trends encompass a wide range of topics, but security is one of the most important ones. Let’s talk about some of the recent innovations that are bringing together the power of artificial intelligence and AI to take healthcare security to the next level. 

The Role of AI Biometrics in Healthcare

AI Biometrics is often associated with security. Although securing protected health information (PHI) is extremely important, it has other uses, such as preserving patient data integrity. Let’s talk about some of these applications. 

Patient Matching and Identification

Shifting to electronic health records (EHR) has called for a variety of changes to the healthcare industry. In order to make accessing and sharing health information safer and more efficient, biometrics steps into the forefront. Biometrics enables secure digital identity verification and ensures that healthcare information is exchanged with privacy in mind. 

EHR makes it possible to store more records than ever in huge databases. However, drawbacks exist. For example, the Harris Health System has 528 patients with the name Maria Garcias that have the same birthday. If left unaccounted for, this can leave a huge margin of error when sharing protected health information and prescribing medicine.  

Using fingerprints, facial recognition, and other physical forms of biometrics can streamline this process greatly. AI can make these systems more accurate and safer. A study published in 2020 found that using fingerprints could greatly automate the process of identifying patients. 

Medical Biometrics in Data Security

Logging into and out of systems can be time consuming. Single Sign-On (SSO) can help get around this issue while remaining secure, but it isn’t 100% secure. AI biometrics provide the solution. Authenticating oneself using fingerprints, facial recognition, iris scanning, or another method can allow users to log in more quickly without sacrificing security. This is effective for telehealth, which requires convenient and secure authentication for both providers and patients. The demand for the passwordless solutions has only risen since the beginning of the COVID-19 pandemic.

How Artificial Intelligence Improves Biometric Authentication

Biometrics make convenient and secure access to systems possible. However, they aren’t perfect. Facial recognition systems can be fooled, fingerprints can be synthetically recreated, and voices can be emulated using deep learning. Vulnerabilities in biometric authentication call for additional measures to ensure that protected healthcare information remains in the right hands. Just as artificial intelligence finds vulnerabilities in these systems, AI can be used to fight back. 

Fingerprint biometric systems

Fingerprint biometric systems may not be as effective as we think at protecting PHI. Researchers at New York University Tandon School of Engineering showed that many systems are built to accept partial fingerprints. This makes it more likely that an AI-generated fingerprint could fool them.

It’s important to note that they are not attempting to replicate an existing fingerprint, but in fact generating unique fingerprints that have the potential to brute force attack partial fingerprint systems. Researcher Philip Bontrager explains in response to the results that, “These experiments demonstrate the need for multi-factor authentication.”  

Facial Recognition

Using the human face is effective for biometric authentication, however it can be susceptible to exploitation with face spoofing or presentation attacks. This can put protected health information at risk. 

AI can improve facial recognition with various pre-trained deep learning models. Unique models can also be trained given an appropriate amount of data to work with. Importantly, anti-spoofing methods powered by AI can be used to detect if the face is real.

Deep Neural Networks (DNN) for Voice Recognition Security

Voice recognition is another point of vulnerability for biometrics technology. Deep Neural Networks can be used to detect synthetic voices. In 2019, a deepfake voice was used in fraud that cost a company €220,000. This could pose a serious threat to protected health information. 

Thankfully, studies have shown that deep neural networks can differentiate fake voices from real voices. Deep learning models have made it so that voice recognition is, despite attempts to exploit it, a safe form of biometric authentication. 

A case study by MobiDev involving facial and voice recognition technologies shows that multimodal biometric authentication is the future of enterprise security. The software application identifies the user with questions, voice, and facial recognition. It also utilized anti-spoofing techniques to prevent exploitation.  

Evgeniy Krasnokutsky PhD, AI/ML Solution Architect at MobiDev, explains: “Deploying AI-powered Biometric Authentication solutions to a cloud with uninterrupted communication channels and computing power for neural networks is quite convenient and scalable. Artificial intelligence and machine learning can help us make our systems more secure and efficient.”

Adhere Pandemic Restrictions

AI technology can recognize and automatically count visitors entering and exiting a medical venue. This helps adhere to government guidance regarding the safe number of visitors based on square footage and ensure visitors and staff maintaining a safe social distance. Additionally, AI-based face mask detection systems can recognize if a visitor is not wearing a mask. 

AI-Driven Biometric Diagnosis

In addition to all the benefits of including big data in healthcare, AI Biometrics technology now shows promise for medical diagnoses. Eye imaging and speech recognition can be used for diagnosis, as well as ECG sensors on smartwatches. In the future, AI-based behavioral biometrics could be used for aiding in mental health conditions treatment. 

Securing the Future of Healthcare

AI-powered biometrics technology has a great deal of promise in supporting the healthcare industry in its mission to protect patient privacy and identify patients with higher accuracy. With opportunities expanding into the future, investing our time and care into these tools will be important for keeping systems up to date and secure against malicious attacks.