Artificial intelligence (AI) is no longer just a buzzword used in science fiction films; it now has real-world applications. Today, the technology is used for predictive analytics, data science, software automation testing and mobile computing processing. The big question, however, is how the application of AI in healthcare can be beneficial and what milestones can it still achieve in the future.

AI technologies have a reputation for eventually being self-reliant. While this may still be many years away, its present iteration already provides a lot of utility to all stakeholders. 

Today, we’ll be taking a closer look at the use of AI in the healthcare industry. We’ll also discuss its specific use cases that have enabled healthcare professionals to provide better diagnoses, treatments, and patient care.

By the time you’re done reading, you’ll know exactly how automation and machine learning fit into the overall healthcare system. You’ll also have a clear understanding of what the future holds for the continued development of this exciting technology. Let’s get started!

AI in healthcare – How it works

Initially, AI technology helped automate processes across various industries that were deemed to be redundant and monotonous for human labor.

For instance, early applications of AI in the auto service sector involved merely collecting and analyzing data. This provided basic information to repair shops about the cars, their service history, and their owners.

Now things have advanced far beyond this level. It has developed the ability to prevent accidents from happening.  It is now even capable of analyzing a specific driver’s driving habits and the overall health of their vehicle. Based on this analysis, it can then make recommendations such as when to repair a car’s brakes – immediately or after they had traveled a certain distance.

AI is useful in accident situations as well because the visual inspection process for auto damage has been automated. It can determine the extent of damage and help insurance companies in providing photo-based estimates of repairs.

The healthcare sector has also seen an evolution of AI in a similar way. By digitizing health records, AI has also effectively cut down the use of paper significantly. It also helped maintain an easy flow of data to insurance companies, hospitals, and patients.

Make no mistake, AI is constantly being improved but has shown consistency in its evolution to expand its applications. From improving back-office productivity to becoming a facilitator for bettering healthcare outcomes, AI has come a long way.

AI has led the way in exploring new treatments, developing new models, and developing vaccines during the Covid pandemic. In addition to enhancing patient outcomes and experiences, AI-based systems can identify adults and children wearing face masks and measure social distance standards.

AI systems work by analyzing vast amounts of healthcare data. This data can be in the form of clinical research trials, images, and medical claims. It then locates insights and changes that are commonly undetectable by manual human skill sets. 

AI algorithms are taught using deep learning and label data patterns. Deep learning also analyzes and interprets real-time data with the help of extended knowledge from computers. 

The implications of AI in healthcare are huge. Based on a few reports, artificial intelligence and neural networking systems in healthcare will be valued at $6.7 billion this year. It’s critical to understand the current impact of AI and potential future developments in light of this significant growth spurt.

Here’s everything AI helps within the healthcare industry in a nutshell:

  • Clinicians can improve and customize patient care strategies by collating patient data and can then predict or diagnose diseases faster.
  • Healthcare payers can tailor health plans by leveraging AI-powered chatbots with other people looking for customized digital health solutions.
  • AI can greatly speed up medical coding search and confirmation for researchers, clinicians, and data managers responsible for clinical trials. This is greatly important in conducting and concluding clinical studies.

Now let’s dive deep into various applications of AI in healthcare and how they can benefit the medical care ecosystem.

Applications of AI in healthcare

The presence of AI is becoming crucial for healthcare. Since we have established that, we will move on to the where, when, and how of it all. Read further to get a full grasp on the applications of AI in this department.

1. Assistance with Natural Language Processing

AI experts have been trying to understand human language for a long time. This field, NLP, incorporates applications like:

  • Interpretation
  • Text examination 
  • Discourse acknowledgment 
  • Different objectives connected with the language

To understand NLP better, let’s look at a sector where it has the best implementation – the stocks and equity markets. Traditionally, quantitative data was used to make predictions for future prices. 

Now NLP is used to make pricing forecasts by assessing the market sentiments. This is accomplished through in-depth analysis of stock market news, financial documents, and social media. It then converts the text to a sentiment score. In the next step, this score is used for price forecasting and generating buy and sell signals.

Similar NLP support is sought after by the healthcare sector as it works to automate its processes. In medical services, the predominant utilization of NLP includes the creation, understanding, and characterization of clinical documentation and distributed research. NLP frameworks can:

  • Conduct conversational AI
  • Interpret patient associations
  • Get reports ready (e.g radiology assessments)
  • Investigate unstructured clinical notes on patients

2. Construct complex platforms for drug discovery

AI algorithms can identify novel therapeutic uses for drugs and track both their toxicity and their mechanisms of action.

It can also allow for the foundation of multiple drug discovery platforms. These platforms can efficiently gather information on already-marketed medications and other bioactive substances.

Additionally, these platforms and AI tools can process multiple terabytes of biological data every week. This data amounts to millions of clinical experiments weekly too. All this is done by utilizing the core concepts of chemistry, data science, and genomic biology and is driven by automation. 

Once this biological dataset is collected, machine learning tools can create insights that are too complicated for humans to construct. Furthermore, this method of drug discovery decreases the risk of human bias.

3. Supporting medical imaging analysis

AI is used for case triage since it supports clinicians in reviewing images and scans. It gives cardiologists and radiologists the means to identify vital insights for prioritizing important cases. It can also help avoid errors in interpreting electronic health records (EHRs), and help establish the practice of accurate diagnoses.

Large amounts of data and images collected in clinical studies require checking and evaluation. AI algorithms can quickly sift through this data and compare it to similar studies to identify out-of-sight connections and patterns. This method can help medical imaging professionals track vital information quickly. 

AI can also use past diagnostics and medical procedures, data on potential allergies, medical history, and lab results. It then delivers this information to healthcare professionals with a summary that highlights the context for these images.

4. Help the emergency medical team

During an unexpected cardiovascular failure, the time between the emergency call to the rescue vehicle appearance is significant for recuperation. 

Emergency personnel should have the ability to recognize heart failure’s effects in order to take the appropriate precautions for increased endurance. Computer-based intelligence can break down both verbal and nonverbal pieces of information to produce an indication.

There are certain AI medical devices that help crisis medication staff. They can caution crisis staff on the off chance that it identifies a cardiovascular failure by:

  • Background noises
  • Investigating the voice of the caller 
  • Important information from the clinical history of the patient

Like other ML advances, they don’t look for specific signs. In fact, they train themselves by paying attention to calls to devise a pattern and recognize important variables.

Because of this learning, these devices work on their model as a continuous cycle. The innovation these applications are furnished with can recognize the distinction between background commotion. 

A study conducted in 2019 uncovered the abilities of ML models. They use speech recognition platforms, ML, and other background hints to better understand heart failure calls than human dispatchers.

ML can assume a fundamental part in supporting crisis clinical staff. Later, clinical units could use the technology to respond to emergency calls using drone-equipped defibrillators or with CPR-prepared volunteers. The opportunities for endurance in cases of heart failure would increase as a result.

And its utility doesn’t end here. It can also help clinicians and crisis clinical staff to bolster timely responsiveness in their departments. A healthcare professional may spend up to one-sixth of their working hours on administrative tasks. As a result, there is less time available for patient care and more time is spent on unproductive tasks. 

AI can help them in strategizing time more effectively by removing or significantly reducing time spent on repetitive administrative tasks. These extra minutes are crucial in medical emergencies because they can help prioritize the cases and save lives.

5. Analyzing unstructured data

Clinicians don’t always remain updated on medical breakthroughs and advancements. It is mainly due to large swathes of public health data and medical records that keep them occupied. Imagine trying to parse through mounds of financial documents manually. Such tasks take time.

Medical data is frequently stored as complex unstructured data, making it challenging for healthcare providers to access and understand. Similarly, EHRs and biomedical data can also be a minefield to navigate. 

AI can curate this data from medical units and professionals and then promptly scan it using machine learning technologies. It can then provide immediate and reliable answers to clinicians.

It is one area where AI can make data parsing easy by: 

  • Assisting with repetitive tasks
  • Standardizing medical data regardless of the format
  • Helping clinicians with accurate, fast, and tailored treatment plans for patients

6. Support health equity

The AI and ML industry should plan medical care frameworks and devices that guarantee rationality and balance are met. And for it to deliver the best outcomes, it must occur in both data science and clinical examinations.

With more utilization of ML calculations in various areas of virtual health, the risk of health inequities can decrease. Those tasked with implementing artificial intelligence in healthcare must ensure that AI calculations are accurate, objective, and fair. 

ML includes a number of techniques that enable computers to benefit from the data they process. On a fundamental level, it means that ML can provide impartial forecasts to some extent if it solely relies on an unbiased analysis of the underlying data.

Artificial intelligence and machine learning calculations can be taught to diminish inclination. It can be accomplished by increasing data transparency and the capacity to reduce health disparities. Medical services research in AI and ML can dispose of health results discrepancies because of race, nationality, or orientation.

7. Use data for predictive analytics

With AI-driven apparatus and apps, clinicians are able to be more strategic with their workflows, clinical decisions, and treatment plans.

NLP and ML can peruse the whole clinical history of a patient continuously. It then interfaces it with side effects, persistent affections, or a sickness that influences different individuals from the family. 

For the elderly and vulnerable patients, this data can work hand in hand with the medical alert systems. It enables them to maintain their independence for longer by receiving care from clinicians and caregivers remotely. 

Putting it in another way, medical alert systems were traditionally designed to seek help after an accident. They have been transformed into solutions to persistent illnesses that can be anticipated and their progression rate can be followed. 

This information is then utilized by EHRs as a source to produce choices for clinical experts. It takes into consideration information-driven choices to work on understanding results. They can transform the outcome into a prescient investigation device that can treat a sickness before it becomes serious. 

The future of AI in healthcare

Artificial intelligence has a significant part to play in medical care contributions representing things to come. In the form of machine learning, it is the essential capacity behind the improvement of medical accuracy. 

Albeit early endeavors at giving diagnosis and treatment have demonstrated to be difficult, we expect AI will eventually dominate that space too.

It is not whether the advancements will be adequately competent to be useful that will be the best test for AI. The real challenge will be guaranteeing their adoption in day-to-day clinical practice.

For broad reception to take place, AI frameworks should be:

  • Educated to clinicians
  • Supported by regulators
  • Work in much the same way
  • Updated over time in the field
  • Coordinated with EHR frameworks 
  • Paid for by the public or privately-funded associations
  • Normalized to an adequate degree than comparable products 

These difficulties will eventually pass. However, they will take significantly longer to do as they are reliant on the technology’s overall maturity. 

It likewise appears to be progressively evident that AI frameworks won’t supplant human clinicians on a broader scale. Instead, they will broaden their efforts in order to better concentrate on patients. 

After some time, human clinicians might advance toward job designs that draw on interesting human abilities like compassion and persuasion. 

Here are 3 implementations of AI initiatives we may see in healthcare soon:

I. Robotic surgeries

Artificial intelligence and cooperative robots will change medical procedures concerning their speed, and capability while making delicate cuts. Since robots don’t get tired, the issue of exhaustion in extended and vital surgeries isn’t a problem.

AI machines have the capability for utilizing information from past tasks to foster new surgical procedures. The accuracy of these machines lessens the chance of any accidental shakes and tremors mid-procedure. 

II. AI predictive care

Artificial intelligence and predictive intelligence will assist us with understanding the various variables in our lives that impact our well-being.

It’s not just about when we could get the season’s virus or what ailments we’ve acquired. It will be about the things connecting with where we live, what we eat, where we work, and what our nearby air contamination levels are. In fact, it will go a step further and consider how our finances stand and whether we are so deeply in debt that trying to avoid bankruptcy is causing us to lose our sanity.

Medical care frameworks will guess when an individual is in danger of fostering a constant sickness. Based on these predictions, they will recommend protection measures before it gets worse. This advancement will find success to the point that rates of diabetes, congestive cardiovascular failure, and COPD will decline.

III. Networked hospitals

With predictive care comes one more advancement related to hospitals and clinics. These institutions will no longer be large structures that cover a wide scope of diseases.

Instead, they will divulge all resources to care for the intensely sick, while less critical ones might be treated through more modest approaches.

These places will be wired into a single digital network. Centralized command centers can then dissect clinical and location information to screen supply and demand across the network. 

In addition to utilizing AI to detect patients in danger of worsening, this method can also eliminate bottlenecks in the system. It can guarantee that patients are directed to where they can best be cared for. Similarly, medical services experts will be sent to areas where their services are required the most.

Utilizing AI can better connect hospitals and healthcare organizations to a centralized network. It is set to become the de facto solution to help all stakeholders work better as a team. 

Conclusion

AI in healthcare isn’t a scenario set for future implementation but is already widely used today. Alongside medical professionals and healthcare services, AI and its big data neural networks have the potential to revolutionize the industry. 

With better networking, robotic surgeries, and predictive care, AI has a bright future in the medical industry.

We hope the post was an insightful read into AI and how it can continue to be beneficial in healthcare. 

To learn more about how you can leverage our healthcare intelligence to gain dependable data for the growth of your organization, please visit our website or email us at [email protected].