10 Innovative Uses for AI in Healthcare

The most innovative applications for AI in healthcare address modern problems. While many medical facilities have already explored using the technology in the diagnostic process, they have yet to tap in to most of its potential. Although it’s somewhat of an unexplored frontier, many exciting, practical developments are occurring.

1. Bridging Language Barriers

According to one study, over 25 million Spanish-speaking people in the United States reported receiving less care than others due to language barriers. Specifically, they have fewer hospital visits and doctors prescribe them less medication.

While federal regulations require healthcare facilities to have translation services, they typically don’t enforce the mandate. Additionally, insurers often won’t cover the cost of a dedicated translator. As a result, many people have poor-quality visits or go untreated.

Natural language processing (NLP) may be the solution to this issue. It’s a branch of artificial intelligence (AI) that essentially understands language. Healthcare professionals can teach medical terminology in multiple languages to improve communication between themselves and their patients. 

Since it responds nearly instantaneously in real time, it is an incredibly effective communication tool. It can even support those who already have translators, as it can break down complex medical jargon in a way that the patient can understand.  Ultimately, it would make healthcare more accessible to those in need. 

2. Expanding Telehealth

Most people are familiar with chatbots because customer service departments around the globe rely on them for prompt communication. How would they fit into healthcare? AI can replace doctors and provide answers or communicate the likelihood of specific diagnoses.

In fact, AI models are surprisingly effective at identifying diseases. For instance, one machine learning model from Washington University had a 90% accuracy rate when identifying signs of ovarian cancer. 

People could simply hop on their phone or computer to talk to a chatbot for half an hour instead of scheduling an appointment, taking time off work and possibly exposing themselves to illness in the facility’s waiting room. AI in healthcare increases accessibility.

This method also protects medical professionals from disease exposure, which would be critical in a high-risk situation like an endemic. Additionally, it could alleviate some of the pressure on them by reducing their workload. 

3. Utilizing Medical Wearable Data 

More hospitals began using the Internet of Things (IoT) during the pandemic through medical wearables. Since they collect patient data to track their conditions, they’re effectively a valuable untapped resource for AI. Algorithms could use them for training, allowing them to personalize predictive care.

Since the devices update in real time, AI will always have up-to-date, accurate information. As a result, it can reference its dataset to make predictions about their health. Imagine a person finding out they were at high risk for a heart attack before it becomes serious — this technology could help people improve their lifestyles or seek medical care far in advance of a life-threatening situation. 

4. Producing Synthetic Data

While clinical trials are beneficial, they’re often plagued with inaccuracies. A medical professional at England’s National Health Service studied 150 clinical trials in 2020, finding that 44% contained impossible figures, duplicates or incorrect calculations. They also typically faced issues like convenience sampling or small sample sizes. 

On top of this, the industry often strictly limits data access, preventing research from benefiting as many people as it should. AI is the best modern alternative available because it can produce synthetic data. Essentially, it can use historical and current information to create an artificial knowledge base. 

Researchers and medical professionals could use it to further studies, improve trial accuracy and train students. While it may not be a 100% accurate reflection of reality, it is one of the best solutions to overcome the current issue with data silos. Also, the industry could save $108 billion annually with an efficient research and development process.

5. Revolutionizing Post-Operation Care

The capabilities of AI in healthcare are seemingly endless. For example, post-operation care is an innovative use for NLP. Patients often struggle when they get home from surgery — or even a routine visit — because they lack knowledgeable support systems. They may forget to take their medicine, take it improperly, experience intense pain or face new side effects. 

The ideal solution would be to assign professional helpers to them, but that’s simply unrealistic. An AI assistant is much more accessible, affordable and effective. Using NLP, it could give them reminders, tell them how to alleviate pain and alert their doctor of any undesirable changes in their condition.

6. Allocating Resources Properly

The healthcare industry is wasteful. For example, clinical operations alone account for $165 billion in resource waste each year. AI can help address this issue by identifying the best course of action. 

Consider, for instance, the distribution methods for the COVID-19 vaccine. It was scarce, so there was a lot of debate about which communities should get it. Additionally, professionals even had to get rid of thousands of doses at times because of improper management. 

In this case, AI could’ve identified which communities had the highest likelihood of spread, making the process much more efficient. Whether the medium is hospital beds, drugs or vaccines, it can make data-driven decisions to guide professionals.

7. Identifying Potential Outbreaks

If AI leverages large amounts of data, it can identify potential disease outbreaks, allowing professionals to address them before they become a threat. It can use historical and current information to recognize hidden patterns before medical professionals can.

Imagine a world where an algorithm could recognize a suspicious uptick in similar symptoms and alert the relevant department before anyone even knew of the disease’s existence. In this scenario, it could analyze each patient’s personal history to see what connects them, meaning it could find the outbreak’s source.

If this technology is extensive enough, it could prevent another pandemic. Of course, it would need big data to fully realize its potential. This would realistically require facilities to share their information on a massive scale.

8. Supporting Doctors

Misdiagnosis adversely impacts millions of people in the United States every single year and costs the industry billions of dollars. Often, medical professionals make subconscious assumptions, preventing them from making an accurate diagnosis.

Since AI is data-driven, it could dramatically increase accuracy. While it is still prone to training on biased information, it’s typically much more effective than a human-only approach. It could analyze their symptoms, calculate the likelihood of each and provide a comprehensive list of the most possible issues. 

Additionally, a model could support medical professionals during their day-to-day operations. For instance, it can reduce MRI time by 90% on average through its rapid image acquisition, freeing up time for more pressing matters. The potential for AI in healthcare to be revolutionary is incredibly high.

9. Improving Cyber Resilience

Every AI tool added to a hospital’s tech stack represents another potential entry point for cybercriminals, and healthcare has long been a prominent target for hackers because patient data is highly valuable. 

The uncomfortable truth is that governance hasn’t kept pace with adoption. Roughly 16% of healthcare organizations currently have system-wide AI governance frameworks in place, which means most facilities are integrating AI faster than they’re building the proper infrastructure to protect it. 

Thankfully, AI can work on both sides of this problem. Instead of just being a new vulnerability, it can actively monitor networks for unusual activity, flagging potential intrusions long before a human team would. 

It can also track how patient data moves internally, flagging anything that appears to be accessed or shared improperly. As AI becomes more embedded in diagnostics, this kind of built-in defense will likely be just as important as the technology it protects. 

10. Catching Pediatric Health Risks Earlier

Children’s symptoms don’t always follow the same pattern as adults’ do, which makes early diagnosis in pediatric care especially difficult. Subtle warning signs can be missed simply because a condition presents differently in a five-year-old than in a grown adult. AI is starting to change that. Emerging models are processing multivariate data across health and educational domains, allowing them to identify early patterns in pediatric development.

This targeted approach matters because children can’t always describe what they’re feeling, and parents don’t always know what to look for. An algorithm trained to recognize early markers of a developing condition could alert a pediatrician earlier in the care process, giving families a head start on treatment rather than a delayed diagnosis.

The Technological Revolution of AI in Healthcare

How can AI in healthcare innovate traditional medical practices? It can overhaul communication, streamline diagnoses and support practitioners in a unique way. Ultimately, it has the power to change the system for the better.