Workforce shortage, heavy workload, and salary disparities have never been more poignant in healthcare than they are today. It’s safe to say that doctors and nurses can’t deliver good care to their patients if they neglect their own health. In the circumstances, vendors and healthcare advisors and workers have high hopes of integrating technology into healthcare. Technology can ease the workload of healthcare workers and transform the way patients are diagnosed and treated.
Slowly but surely, Artificial Intelligence (AI), Deep Learning, Machine Learning, the Internet of Things, Natural Language Processing (NLP), and Face Recognition are becoming the new norm in the healthcare sector. Today, healthcare providers can deliver excellent patient care by blending their competencies and innovation.
According to Grand View Research, the global AI healthcare market is expected to grow at a compound annual growth rate (CAGR) of 41.5% from 2019 to 2025. The advantages of AI in healthcare are enormous. AI is used in healthcare in diagnostic and drug discovery labs, operating rooms, intensive care units, and more. And it’s just a start.
Get a glimpse of the market:
Using AI to Protect Sensitive Data
AI integration strategy in clinical practice isn’t always taken warmly. The reputation precedes AI and most people see it racially-biased, weirdly inhuman, and threatening to traditional jobs. But is it? It has drawbacks as most novel technologies do, but its pros do outweigh the cons.
Take data in healthcare. It’s diverse and complex. And on top of that, it’s hard to store and keep safe. Since the healthcare sector generates plenty of data that is an easy target for hackers, AI offers its watchful eye. According to a 2019 Ponemon report, 73% of organizations are understaffed and fail to detect and respond to data breaches fast. HIPAA Journal stated that 2019 brought about an increase in data breaches. 41,232,527 patient records were disclosed and stolen in 2019.
Source: hipaajournal.com
The total number of data breaches reported went up to 36.12% annually, from 371 breaches in 2018 to 505 breaches in 2019.
With millions of healthcare records breached so far, hospitals are seeking a robust solution to guard the patients’ data. This is where AI comes to aid. Today, one of the key objectives of artificial intelligence in healthcare is to safeguard and transfer sensitive data securely. AI solutions have the power to automate malware analysis and threat intelligence.
Facial Recognition in Healthcare
Face recognition technology aims to identify a person based on their unique facial landmarks and skin texture. Then the algorithm checks it against the database on a number of matches. The procedure is easy. It requires any device that has a camera.
Source: unsplash.com
There come plenty of security identification solutions like passwords, two-factor authentication, fingerprint identification, and more. But none of them is as strong as facial recognition is. The technology is based on math patterns. They keep the data safe and sound.
Face recognition is a subset of AI. It is used in healthcare for plenty of purposes. Take a closer look at the key technology use cases:
Key Use Cases of Face Recognition in Healthcare
Patient Check-in and Check-Out Procedures
Patient identification solutions have recently gained momentum. They simplify the whole patient check-in process and free hospital personnel from paperwork. More importantly, face recognition-based solutions help correctly identify patients and eliminate wrong procedures and wrong-patient errors. The latter can result in severe temporary or permanent harm or death.
Facial recognition software in healthcare is the right inventory to tackle any possible patient impersonation. If it’s incorporated into a video surveillance system on the hospital premises, it can become an efficient tool to spot flagged or wanted people, drug dealers, and other criminals. By scanning the patient’s face and checking it against the hospital database, the technology helps verify the person’s identity and prevent any fraud like someone impersonating to get expensive medical treatment or drug dealers infiltrating hospitals.
Diagnosing Diseases and Conditions Using Face Recognition
Face recognition has hovered almost all healthcare domains and the diagnostic process is no exсeption. Healthcare evangelists and advisors claim that in the coming years, health mirrors will be in high gear. The term “health mirror” refers to a medical mirror that uses lights to measure vital body signs. In reality, the mirror itself can be easily replaced with a laptop or a phone camera. The main idea behind this is that we’re all almost at the point where telemedicine is bringing care closer to our homes. With face recognition health apps, your health status is just one face scan away. By simply looking at the camera, a person can measure the heart rate, blood pressure, stress level, and more.
Thanks to face recognition, the technology is contact-free and non-invasive. Which is a big advantage, when it comes to children or people with sensitive skin checkups.
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Computer-aided diagnostics (CAD) is also gaining popularity in the healthcare industry. For example, using custom image recognition software in the diagnostic process helps make fewer errors. Machine learning-based approaches allow doctors to uncover abnormalities in imaging data and accelerate the treatment. Blending computer vision with diagnostic expertise, doctors can deliver greater results. For example, they can localize and track joint angles and velocities. It encourages better response to rehabilitation and faster patient recovery.
Face Recognition Against COVID-19
As the pandemic sweeps the world, healthcare advisors are seeking a way to stop the spread of the deadly virus. AI and face recognition technologies are at the frontline to make it happen. Face recognition enables tracking down people who are on quarantine with an app. The app occasionally asks for selfies to prove that people are staying indoors. And there’s more to it. To enforce quarantine measures in public places, face recognition is used for identity verification purposes. It’s non-contact, which means it’s safe.
If you want to understand how can AI be used in healthcare to win the war against the coronavirus, take a closer look:
Emotion Detection in Mental Therapy
Another use case of facial recognition technology in healthcare is real-time emotion tracking. At the beginning of its potential, emotion analysis can be used in the mental healthcare industry. For quite some time, the industry had been marked by tech stagnation. But now it’s being disrupted by emerging technologies. Today, combining facial recognition with evidence-based therapy (EBT), mental disorders can be treated as nearly as good as physical ones.
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Using facial recognition for mental health purposes, patients can get personalized, patient-centered, efficient, and timely care. The next-gen technology is used to track facial landmarks and cues to interpret the patient’s inner feelings.
Face-to-face therapy has a lot to offer:
- fair patient emotion assessment
- personalized therapy
- access to mental care (app check ups instead of seeing a mental healthcare provider)
- real-time harmful habit detection (lip biting, cheek biting, eye rubbing, etc)
Opting for a Conclusion
Facial recognition technology is disrupting the healthcare industry. From computer-aided diagnostics, emotion recognition in mental therapy to protecting sensitive data, and combating the spread of COVID-19, face recognition has flattened it all. In the next coming years, technology will realise its full potential and gain more momentum.
Smart Up with a Custom Face Recognition Solution
Need a patient identification system or a symptom checker app based on face recognition? Contact us at info@indatalabs.com. We’ll be glad to help.
