The Branches of Artificial Intelligence Advancing the Healthcare Industry

The realms of artificial intelligence (AI) are diverse and vast. Once a plot point for science fiction movies, this technology has evolved into a concrete force revolutionizing the world around us. From education to automobiles to finances, every industry feels its influence. 

There are various reasons for its wide adoption. AI systems leverage their capabilities in data processing, automation, and decision-making to improve business operations. But as research goes deeper, scientists are beginning to unlock this intelligence’s full potential. 

When innovative thinking and human expertise are combined with machines, progress is made that propels us into the future. To demonstrate this further, we will look closely at the healthcare industry and which powerful branches of AI contribute to its advancement.

Why does the healthcare industry need AI?

Healthcare systems have not yet been perfected. For instance, 42% of adults worldwide reported long wait times as a leading problem. In the same report, 42% mentioned a lack of staff, 20% stated a lack of investment, and 31% chose the cost of accessing treatment. 

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These issues remain prevalent in the industry. To reduce them, the practices and regulations found in traditional systems need to be modernized. Not doing this can hinder innovation because there is no room for new solutions and methods. 

The diversity of AI is helping providers, policymakers, and stakeholders concentrate on accessible, equitable, and contemporary services. Let’s see where the technology is making its impact and how likely the applications are to become a healthcare standard. 

1. Machine learning 

AI has come far already. Just look at the machines mimicking human intelligence. Aptly named machine learning, this branch combines data science and computer systems to grasp information. Once these systems are trained, they know how to adapt to new knowledge without extra instruction. It works in different ways, which you’ll see below:

• Supervised learning: labeled data teach algorithms to predict outcomes.
• Unsupervised learning: finds patterns or groups in data without guidance. 
• Reinforcement learning: the algorithms learn through a trial-and-error method. 

These diverse capabilities led to machine learning algorithms being used for incredible breakthroughs like self-driving cars. Here’s how they have begun to change healthcare.

Predictive maintenance 

Maintenance isn’t something most organizations are prepared for. Many will schedule it monthly or yearly, depending on the equipment. Or, the repair man might have to pay a visit when something breaks or fails to perform normally. The result of this unpreparedness is rescheduled hospital procedures, unplanned downtime, and expensive fixes. 

Also, if the broken item is a specialist tool, it could take a long time to replace it. It might need to be shipped from another country or even built from scratch. Operations would halt, and wait times would become even longer than usual. Wouldn’t it be a life-saver if you could identify potential failures and act accordingly before intervention was needed?

That’s what machine learning can do. Typically through regression models and decision trees, algorithms learn from historical data and make predictions. From this, users can be more proactive and improve the overall productivity of the team. 

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If every healthcare institution implemented this strategy, how could the industry advance? Let’s look at the valuable benefits to find out. 

• Better resource allocation
• Less money spent on overnight or emergency fixes
• Improving the life-span of resources
• More money to spend on updating older equipment 
• Staff can focus on their tasks and not the consequences of breakages

The clue is in the name. Predictive maintenance makes data-driven and calculated assumptions but the results aren’t guaranteed. Regardless, it can be a way of scheduling maintenance more effectively and being prepared for problems before they manifest. 

Medical imaging analysis

Medical imagery is crucial. It promotes non-invasive exploration into a possible concern, leads to early detection, and helps patients to visualize their diagnosis. Machine learning models have an important role to play in improving specific technologies like this one. Analysis not only becomes more straightforward, but it can even be fully automated.

Medical professionals wouldn’t need to be present for initial screenings. They would also have their workload cut in half because they don’t need to spend days examining one scan. Below are other areas that get a much-needed upgrade.

• Image recognition. The results from an MRI scan are typically interpreted within 24 hours by a person. However, this can be shortened with machine learning models because they are quicker and have amazing decision-making abilities. Doing so elevates anxiety, frees up staff time, and allows for more to be done in a day. Machines will classify whether the image is normal, abnormal, or high-risk with speed beyond even the most veteran team member.  
• Efficient monitoring. Expert systems, or knowledge base systems, are machine learning models that can gain experience. Therefore, if you were to feed these several scans of a patient over time, the inference engine would be able to draw new conclusions based off of past data. It would be possible to track anomalies and paint an accurate picture of disease progression. The radiographer will be alerted when intervention or changes to a medical plan are necessary.
• Improving images. Poor-quality scans are inevitable when healthcare institutions cannot afford to invest in new equipment. However, this can lead to stressful misdiagnoses and long interpretation times. This technology successfully enhances or refocuses the photography to give clarity and reduce error. 

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AI could standardize machine assistance across healthcare facilities. Doing so would help everyone access the best possible treatment regardless of the resources, available specialists, or average income. With this technology, we’ve never been so close to equity. 

2. Natural language processing 

Natural language processing, conveniently abbreviated to NLP, is a subfield of machine learning techniques. It combines this type of AI with linguistics and computer science to build systems for speech recognition and human language comprehension. 

Apple’s Siri exists because of these models. Without them, we’d have a less sophisticated voice assistant on our iPhones. This might not have as many obvious applications as other branches, but there are plenty. Below are some of the most useful to prove it. 

Companionship 

Cast your mind back to the years of COVID-19. Hospital visiting arrangements changed drastically to give patients the best chance at recovery. At various points during the pandemic, loved ones were either heavily restricted or not allowed on the premises at all. Friendly volunteers stopped coming in and nurses had to prioritize their health over a chat.

Even in late 2022, in clinics in Florida, people were still being encouraged to stay home. Naturally, this had huge consequences for patients. Other problems became apparent during this period, which we will list below. 

• Overwhelming loneliness
• Deteriorating mental health
• Feelings of abandonment
• Resistance to treatment
• Experiences of depersonalization

For many, COVID-19 has become nothing more than a bad memory. However, this is still a significant problem in the industry. For example, there are still people without families and people with diseases that need to be quarantined. They’re more likely to stay in the hospital for longer if they experience the above, putting a strain on healthcare systems. 

The AI solution to this problem lies in natural language processing. Specifically, the chatbots that use them. Due to their ability to hold a realistic conversation, they can provide a sense of connectedness. Machine translation also lets these models bridge language gaps, so no one has to miss out on faithful companions. 

Sensely created their avatar-based product, affectionately named “Molly”, to do just that. This virtual nurse engages in chit-chat while assessing the user’s mood. This helps generate appropriate answers. It even answers health-related questions to reduce anxiety when people can’t be there. Below is one of the application’s happy customers. 

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Patient support

In a similar capacity, virtual assistants have been developed to offer guidance and support to those who might not get it elsewhere. For example, elderly patients are not always the most tech-savvy, but a lot of healthcare providers are becoming dependent on them. If self-check-in kiosks and mobile appointments take over, help could become inaccessible. 

A 2022 survey showed that four out of ten Americans can’t access treatment when they need it. A system like this would work to eliminate certain barriers like the one above. Here are some other examples of patients likely to benefit from this technology.

• People who have knowledge barriers 
• Individuals who cannot afford transport
• Anyone with a disorder like agoraphobia
• Those who need to discuss a sensitive topic
• Patients who’ve had traumatic past experiences with healthcare providers

A virtual assistant is the perfect answer. It can instantly answer questions, be a single source of truth in stressful situations, and offer self-help information when appropriate. No more waiting in lines, reading dodgy websites, or being put on hold. 

One example of this technology in action comes from the impressive clinical AI known as Ada. The virtual assistant can do everything from providing personalized guidance to helping users understand their symptoms. Through a chat-like interface, users will receive things like health assessments and recommendations to enlighten their next steps. 

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While it shouldn’t replace nurses, it could be a valuable tool in triaging patients. It also might make people less likely to turn up at the ER when they don’t need to, reducing burdens on providers. Most importantly, it is one innovative resource closer to equal healthcare worldwide. 

3. Robotics

If Nasa is doing it, it’s bound to lead to innovation. The robotics industry combines AI and engineering to focus on the conception and uses of robots. These physical machines can complete tasks either autonomously or with limited human intervention, depending on the context. They won’t necessarily be a replica of C-3PO, though they can be just as helpful.

Without diving into space fiction, let’s examine the areas of healthcare where robotics have made the biggest impact. 

Robot-assisted surgeries

Nurses and doctors are invaluable parts of the healthcare industry. However, they are human. As such, they are predisposed to making the same errors as the rest of us. Unfortunately, biases, emotions, and incomplete understanding can’t always be avoided.

Robot advancements bring incredible developments to surgical processes. They enable surgeons to make decisions based on facts and data alone. This was put into practice with a game-changing clinical trial by the University College London and the University of Sheffield.

The trial spanned 2017 and 2020 in universities and hospitals around the UK. The AI-assisted bladder cancer removal showed telling results. All secondary outcomes (quality of life, wound complications, mobility) improved with robots or matched the results of open surgery. This means quicker recovery time and generally safer procedures.

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Below are some of the more general reasons why robotics should have a place in healthcare, specifically in surgeries. 

• Robots don’t get tired, elevating the stress of overworked and fatigued workers.
• Machines won’t make decisions based on overconfidence or human bias. 
• Breaks or holidays aren’t needed, which is beneficial to overcrowded hospitals.
• A 2018 study found robots were 10 times more precise than human surgeons.

Remember, this application is about enhancement and augmentation, not job replacement. It is the person controlling the equipment and the robot is the tool. While the machine can perform certain actions independently, the process is still monitored by a human surgeon. 

Physical therapy

One important area of the healthcare industry is physical therapy. This method aims to improve or restore a patient’s mobility so they can live life to the fullest. The uses for this technique include disabilities and injuries. Due to everyone’s needs and goals being unique, this method can require extensive equipment, dedicated teams, and lots of time.

Robotics can contribute massively to physical therapy because they reduce the need for these resources. This gives therapists more opportunities to establish new ways of delivering it. You’ll find some of the most useful examples below. 

• Online rehabilitation. Robot-enhanced therapy can be done online with technology like cameras and sensors. Patients can work from the comfort of their own homes, which is likely to boost their motivation. This is a good solution for those with very limited mobility also. 
• Gait training. These sets of exercises help a patient to improve their ability to walk. Wearable robotics can provide support and stability, meaning the person can do more training for longer without the risk of further harm. 
• Virtual-reality (VR) integration. It’s hard to stay positive when you’re in a blank clinical room. VR combined with robotics can stimulate real-world situations and more engaging scenarios. This tailors the experience to exactly what the individual needs. 

Here’s a cool example. Hocoma is a Switzerland-born company that developed the ArmeoPower, alongside other astonishing robotics products. This one is an advanced arm and hand rehabilitation device designed to support weight and assist with movement. 

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Innovations like this work to personalize healthcare. Machines can adapt and learn with each session, making them the perfect fit for patients no matter what their requirements are. Some AI systems have even evolved to reach the theory of mind, giving them a deeper understanding of thought processes and needs. 

4. Neural networks

Artificial neural network systems use machine learning models called deep learning. The process involves algorithms modeled after the human brain’s neurons (also referred to as nodes) which can mimic its basic functions. As a result, they recognize patterns and interpret information as if they were a person. 

Highly advanced applications like OpenAI’s ChatGPT and Amazon’s Alexa use this technology to function and solve real-world problems. You can imagine how valuable this application will be for the healthcare industry. Let’s dive into some specifics, shall we?

Drug development

Drug development is arguably one of the most crucial elements to advancing healthcare. The research and discovery address unmet needs, keep up with growing medical knowledge, and prevent epidemics. In other words, without this, we’d be in big trouble.

However, the traditional process relies on frustrating experimentation and trial and error. Innovations are necessary to streamline the process. That’s where neural networks come in. They show researchers which candidates to focus on, make accurate predictions, and solve complex problems by exploring data at an impressive pace. 

These are promising big ideas, but let’s take a closer look at the specific applications already being implemented in drug development. 

• Predicting responses. Systems use training data to find patterns in information, they can identify potential adverse drug reactions and side effects. This will improve general drug safety. 
• Drug repurposing. Neural networks can sift through datasets of drug properties and disease information to identify potential relationships beyond what the human expert would be able to see. This enables the discovery of new uses for existing drugs. Reducing the development timeline is then possible because these products can bypass certain clinical trials
• New insights. The data in drug discovery is complicated. Think about chemical structures or clinical data. It wouldn’t be difficult to miss a key connection or misunderstand what you’re looking at. Neural networks take these concerns away by combining and analyzing multiple data types for a more holistic view. 

Here’s an example. A research team in China has developed FingerDTA, a computer framework based on convolutional neural networks. This model is used for drug-target binding affinity prediction. From this, developers can determine if a new drug will bind strongly to its target, therefore being more effective in treating a particular disease. 

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Cybersecurity

Reports show that the healthcare industry experiences a disproportionately large number of data breaches in comparison to other industries. The possession of valuable data about patients and public health could be a cause of this. Someone involved in identity theft or fraud could sell this data for a lot of money. 

Below are some other factors contributing to these intrusions. 

• Lack of staff training 
• Outdated security measures
• Adoption of Internet of Things technology

Dealing with cybersecurity threats is paramount for the healthcare industry to thrive. Even more so as technology rapidly advances. Introducing AI as a crime-fighting partner is the ideal solution. The monitoring of data and mitigating of concerns are even done for you.

• Identifying the perpetrators. These systems can extract a wide range of features and information from an image through facial recognition and computer vision. This can be a useful aid in cybercrime investigations. They enable the: tracking of suspects, generating evidence in court, and monitoring of individuals to ensure they don’t act or enter the premises again.
• Fraud detection. Neural networks extract information from transaction data to find unusual patterns that could indicate fraud. Stopping these actions has the potential to reduce crimes such as the overprescription of medicine for financial gain, falsified medical records, or unlicensed practice. 
• Sentiment analysis. Neural networks can identify the emotions and intentions of people online. When this is applied to social media, for example, analysts can flag suspicious or harmful messaging to expose vulnerabilities. With this knowledge, you can learn what social behaviors prompt attacks and predict the next ones. 

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Technology is becoming smarter every year. As promising as this is, it does mean the risk of cybercrime increases too. Statistics show they are growing yearly. So, to protect the advancement of the healthcare industry, AI methods shouldn’t be implemented alone. A solid security plan includes education, encryption, and unshakable access controls. 

Solutions for the future 

The pandemic of COVID-19 showed us just how important the healthcare industry is. From preventing illnesses and treating conditions to tackling emergencies, it’s undoubtedly a crucial part of our lives. Therefore, we need to be continuously improving and enhancing its processes and performance. One of the most effective ways to do this is by utilizing AI.

We’re able to solve once unapproachable issues with streamlined technology. Keep in mind that these problems, like the ones discussed above, are complex. Most of the time, they’ll require more than a simple resolution. In these instances, you’ll encounter combinations of AI branches complementing each other to create effective solutions. 

There are more branches of AI making huge contributions to our future. If you want to learn what they can do, look at the fascinating applications over at Top Apps.