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Delivering healthcare services at a high level is becoming increasingly work-intensive. It’s an overall expensive and expansive affair due to numerous administrative procedures. That’s why healthcare providers have started implementing the newest AI technologies to ensure the best services for patients and new capabilities for doctors, employers, insurance companies, pharmaceutical firms, and the government. Here, AI comes in with unrivaled enhancements in data analytics, preciseness of diagnostics, treatment prediction building, and relieving medical workers from extra administrative tasks.

So, how does AI fit into healthcare—what’s its ultimate role? Let’s dive deeper into the topic, get to the core of the technology, and review several use cases of AI in healthcare to find out.

How AI Works in Healthcare

AI has proved that it’s far more sophisticated than a human brain in analyzing and segmenting patterns in the vast chunks of data generated by electronic healthcare records, social media, patient summaries, genomic and pharmaceutical data, behavioral and socio-economic indicators, and much more. Healthcare providers feed medical data into the AI, which then explores the data and showcases behavioral patterns and regularities that are simply undetectable by doctors.

Putting AI in charge of identifying and analyzing beneficial medical patterns helps providers develop overall medical approaches—contributing to the field as a whole—and become more efficient and fruitful in the long term. It’s a win-win situation. AI in healthcare may be a recent phenomenon, but it’s reshaping the industry at a staggering pace.

AI in Healthcare — Technology that Reshapes the Industry

Here are some examples of who is benefiting from AI’s current use in healthcare:

  • Clinicians and researchers use AI to expedite clinical trials. AI helps to speed up medical coding search and confirmation, which is crucial during the conducting and conclusion phases of clinical studies.
  • Patients can obtain personalized health plans from their doctor by connecting to a virtual agent.
  • Doctors can use AI to diagnose diseases faster and provide better care to patients.

Technologies Enabling AI in Healthcare

The AI technologies used nowadays in healthcare are just beginning—more ambitious technologies are on the way to involve the combination of multiple data sources, making even greater advantages for pharma and biotech domains. Let’s review different types of AI technologies widely used in practice today. 

Machine Learning

Machine learning (ML) helps the healthcare industry to achieve results in many ways. ML technology has many different versions, but the main idea stays the same—the computer learns something by analyzing the provided data and contemplates it, providing recommendations for the next moves. ML brings more AI in healthcare every year and it does this via several approaches. 

In the clinical field, ML approaches are most commonly based in supervised learning. This ML philosophyrequires certain input data—e.g., a patient’s physical data combined with a database of target patient data—to build further logical outcomes.

Deep learning is a ML subfield that uses complex neural networks to analyze, process, and learn from input data similarly to the way humans learn. Neural networks help classify all the data and receive it in a convenient, hassle-free format to be perceived by medical staff, especially aid practitioners, who use this great tool for narrowing down potential diagnoses to the most probable options.

Optical Character Recognition

Teaching machines to recognize and comprehend visuals similarly to the human brain was a huge challenge for software and hardware engineers. But after years of evolving implementations, we have managed to enrich our computers with vision, achieving even more digitized benefits. 

In the field of healthcare, computer vision can be conveniently used to recognize all sorts of visual data, including hand-written documents that are properly scanned and recognized by a specialized neural network The AI then conveniently translates the documents for medical staff departments in typed format. This and other applications ultimately boost the efficiency of modern healthcare with AI.

Rule-based Expert Systems

Expert systems based on variations of the ‘if-then’ rule were the prevalent technology for healthcare AI since the 80s. In this system, unlike ML, programmers manually encode a vast library of behavioral management to allow the system to operate. 

However, when the number of behavioral patterns starts to exceed several thousand, certain patterns begin to conflict with others, causing the system to crash. Additionally, it’s expensive and laborious to change the knowledge area of this somewhat old-school system. While rule-based systems have their place in AI, providers are increasingly adopting software solutions with ML capabilities in healthcare instead of rule-based systems, especially when data sets become very large.

AI Application in Healthcare

AI in Healthcare — Technology that Reshapes the Industry

At present, within the healthcare industry, AI is being adopted by 46% of those working in service operations, 28% of those in products and services development, 19% of those in risk management, 21% of those in supply chain management, and 17% of those in marketing and sales. In addition, several businesses are developing innovative ways to incorporate AI into smart wearables, devices, and apps, thereby enhancing their efficiency. Let’s take a look at some areas of using AI in the healthcare domain. 

Machine Learning in Diagnosis and Treatment Applications

Years of medical training are required to diagnose diseases properly. Even so, diagnosing can be a lengthy and expensive process. As a result, ML algorithms—and in particular, deep learning algorithms—have been expanding in use to automate disease diagnosis, making it more accessible. 

Current Application of ML in Diagnostic

  • Seeing signs of lung cancer and strokes on computed tomography scans 
  • Using electrocardiograms and cardiac MRI images to estimate the risk of heart diseases
  • Classifying skin lesions in skin images
  • Detecting diabetic retinopathy indications in eye images

It should be noted that doctors are not supplanted by ML or AI— these technologies exist to support physicians in order to improve patient diagnostics and management. Comparisons between AI solutions and physicians would be inappropriate as they do not compete. Future research should compare physicians employing AI solutions to physicians who do not use smart tech.

AI for Drug Discovery and Clinical Trials

Implementing AI into analytical processes involved in drug discovery can save years of effort and millions of dollars to the infamously resource-intensive area.

Current Application of AI in Drug Discovery:

  • Analysis of the biological origin of disease and identifying target proteins for treating the disease
  • Prediction of the molecule suitability and filtration of minimal side effects
  • Identification of suitable candidates for clinical trials

AI can predict the physical and chemical properties of very tiny molecules with quantum mechanics-level accuracy at a considerably reduced time cost. Pharmaceutical companies implement AI in all the stages of drug research, beginning with preclinical trials through data collection and analysis, enabling a more efficient and cost-effective process.

Gene Editing

One of the significant problems in gene editing is that while using clustered regularly interspaced short palindromic repeats (CRISPR) technology, short guide RNAs can randomly fit multiple DNA locations resulting in off-target effects. AI algorithms can accelerate the process of DNA editing by predicting target and off-target effects for a given sgRNA. 

Health Insurance and Administrative Applications

Using the benefits of AI for administrative functions in the healthcare system is not as interesting and revolutionary compared to patient care. However, it has the potential to increase the effectiveness of the bureaucracy.

AI is currently used in claims and payment administration for pairing data across different databases. Insurers and providers must verify whether the millions of claims submitted daily are correct, and automatization of this process helps to increase effectiveness and lower expenses.

We should see further implementation of AI for claims processing, clinical documentation, medical records management, and other functions within several years. This innovation will continue to increase efficiency and lower costs for the company. 

The Future of AI in Healthcare

Human civilization required thousands of years to comprehend basic medical concepts, and now AI is here to change our understanding of medicine and health care. Medical schools and universities around the world can’t keep up with the pace of these changes, and graduates have to adjust to the fast-changing world of medicine. This is evidence of AI’s blinding speed and efficiency in evolving our understanding and practice of healthcare. What does the future hold for AI? Let’s take a look at other technologies that will likely utilize AI in the future to empower and evolve the healthcare industry.

Virtual and Augmented Realities

Virtual reality (VR) and augmented reality (AR) are shapeshifting the look of many different industries. These technologies are evolving fields such as construction, e-commerce, gaming, education, and others.

Imagine how awesome it will be for doctors to get clinical practice by using realistic simulations and putting theories into practice during class. Surgeons, for example, could use VR to facilitate their skills in different situations. Practicing in a simulation will ensure stunning performances during real procedures. Mantling this with AI technology in healthcare will produce great results.

The reality is that using AR and VR technologies to train healthcare workers will increase their competence and decrease teaching time. Using VR to teach healthcare workers has been reported to improve skill retention by 75% and reduce skill fade by up to 52%. 

VR and AR can also help surgeons to reassure patients before significant surgical operations. Many patients fear uncertainty; doctors could destroy these doubts by explaining the whole procedure to the patient using easy-to-comprehend digital instruments.

AR and VR are still in the early stages of development. It will be many years until these technologies are fully incorporated. However, their worldwide use in other industries leaves no doubts that they will be incorporated into healthcare.

Big Data Management

There could be no list of AI trends without mentioning big data, could there? Big data started to gain some traction before 2020. The global pandemic showed the public that healthcare needs analytics to make sense of vast information. The industry quickly comprehended that the absence of accurate data could cause a disaster during the global pandemic.

This understanding led to the increasing demand for accuracy and a subsequent rise in the revenue of different enterprises that offer big data services. It’s an understatement to say that this trend helps to save millions of lives worldwide. Big data also helped countries to plan vaccination campaigns and predict future waves of COVID-19. We believe that the mutual work of big data and AI in hospitals will drastically increase efficiency. 

Customized Mobile Apps

Modern humans can’t comprehend the idea of not using smartphones. Almost every citizen in the world possesses a smartphone today. According to statistics, most people use their phones at least 4 hours per day. Consequently, different apps started capitalizing on this by connecting people all around the globe.

We see the great potential in providing direct doctor-patient communication by using the latest achievements of human technologies. We could see the occurrence of these trends in the early days of the pandemic. Many people were terminally ill before the Covid-19 destroyed the old reality. Having direct communication with doctors was (and still is) the question of life and death for these people. 

Software developers saw the rise of this problem and provided digital solutions. In the future, we will see mobile applications become customized for patients and combined with AI to enhance features like pill and dose reminders, checking blood pressure, heartbeat or other reminders, remote doctor appointments, and more.

Conclusion

Intelligent solutions powered by AI can assist in making better-educated decisions (automation with sophisticated analytics) on the one hand, and reliably detect anomalies (disease screening and diagnosis) on the other—and these are just two main areas where AI may be used. Administrative efficiency and fraud, waste, and abuse (FWA) are two others.

AI and ML will reshape perceptions of wellness and healthcare in many ways. The ability of AI to self-learn and spot patterns in data that are difficult or sometimes impossible for humans to detect is extremely valuable.

In this article, we discussed the role of AI in healthcare and how it changed over the years. We recommend you check out this page to learn how to implement new AI elements into your business.

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