• AI
  • Artificial Intelligence
  • Healthcare
  • Machine learning

Types and Applications of AI in Healthcare

Yura Velichko
2 Dec 2021
7 min
Types and Applications of AI in Healthcare

The healthcare industry is awash with data that it can’t use. 

80% of medical data is unstructured and lies in patient disease registries and images from CT, MRI, and X-­ray machines. With the healthcare system expected to soon be generating more than 2000 exabytes of data each year, it’s no wonder that the industry is turning to AI solutions to help process it all. 

Yet structuring data isn’t the only case where artificial intelligence can come in handy. Cutting down healthcare costs, speeding up diagnoses, and improving patient care are just a few other areas where AI has already shown excellent results. Analysts now predict that AI in the healthcare market will grow from $6.9 billion in 2021 to $67.4 billion by 2027: a staggering CAGR of 46.2%. 

These are the facts, but what’s happening in the industry today? If you’re looking to get a handle on AI in healthcare, here’s a quick introduction to what it offers, along with some common use cases.

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    Types of artificial intelligence in healthcare

    Artificial intelligence mimics human cognitive functions and can make good use of all kinds of both structured and unstructured healthcare data. What are the different types of AI used for this? 

    Let’s take a closer look.

    Natural language processing (NLP)

    Natural language processing (NLP) tools and algorithms can unlock clinically relevant information hidden in piles of human-generated medical records and articles. In healthcare, NLP can help with two main tasks:

    • Speech recognition. It saves clinicians from manually entering EHR notes.
    • Unstructured data processing. NLP algorithms help people interpret information by classifying data, extracting insights, and summarizing them.

    How does this happen? NLP includes five fundamental techniques:

    • Optical character recognition (OCR) is used for digitizing handwritten or scanned clinical notes, medical history records, patient intake forms, etc.
    • Named entity recognition (NER) sorts named entities into predefined categories such as drugs, dosage, diseases, etc.
    • Sentiment analysis reveals the underlying sentiment of a text (i.e., whether the language is positive, negative, or neutral).
    • Text categorization assigns tags to different words or phrases based on predefined categories.
    • Topic modeling is used to group documents based on common words or phrases they include.
    Types and Applications of AI in Healthcare - photo 1

    NLP algorithms first extract information from EHRs or medical documents. They then process this information using multiple techniques (such as OCR, NER, and topic modeling) and classify patients or data into specified groups and subgroups. 

    This process helps doctors find relevant information faster and accelerates clinical trial matching.

    Types and Applications of AI in Healthcare - photo 2

    Machine learning (ML)

    Feeding machine learning (ML) algorithms high-quality structured data allows computers to perform human tasks such as classifying patients, uncovering critical insights, and even making health-related predictions. The more data ML algorithms process, the more accurate the results they give.

    Deep learning is a subset of ML that systems can use to cluster data and make predictions with incredible accuracy. With the help of deep learning, scientists can perform highly advanced tasks faster and more easily. 

    Deep learning deploys different techniques to acquire knowledge. 

    • Supervised learning uses structured, labeled datasets to uncover underlying algorithms.
    • Unsupervised learning uses unstructured data in its raw form (i.e., text and images) to automatically discover hidden patterns in data without the need for human intervention. 
    • Semi-supervised learning uses a combination of a small amount of structured, labeled data and a large amount of unlabeled data to train models.

    Deep learning becomes possible thanks to neural networks that lie at the heart of ML algorithms. Neural networks help recognize patterns to classify and cluster data rapidly. For instance, once trained to detect deviations in cells such as cancer and malicious tumors, AI can improve the accuracy of diagnoses and detect tumors in their early stages.

    Types and Applications of AI in Healthcare - photo 3

    Now that we’ve seen the types of AI technologies used in healthcare, let’s explore how they’re being used today.

    Applications and use cases of AI in healthcare

    Artificial intelligence can positively impact medical practice by improving three of its major components: 

    • Clinical, by enhancing the quality of treatment (e.g., by enhancing the speed and accuracy of diagnoses)
    • Pharma, by fostering new drug development (e.g., by accelerating research and report compilation) 
    • Administrative, by automating revenue cycle management and bureaucratic tasks (e.g., through automated data processing) 

    Let’s take a look at some of the most prominent use cases of AI for healthcare.

    Early disease detection

    Artificial intelligence in healthcare has been shown to outperform doctors in some tasks, such as early cancer diagnosis. Researchers from Babylon Health and University College London proved that their ML algorithm demonstrated expert-level clinical accuracy and performed better than 75% of general practitioners. 

    Another example comes from Google Health and Imperial College London researchers, whose AI algorithm showed higher accuracy than experienced radiologists in detecting breast cancer based on mammograms. 

    Medical imaging

    Identifying health conditions in medical images is one more area where AI is capable of surpassing human experts. Deep learning algorithms can compare MR and CT scans or mammograms to hundreds of thousands of similar cases in a database and identify diseases in their early stages. 

    For instance, researchers from the Kaunas University of Technology in Lithuania developed a new AI algorithm that can predict the onset of Alzheimer’s disease with an accuracy of over 99% by analyzing MRI brain scans.

    Drug development

    AI technologies can accelerate the identification of small-molecule drug candidates and save years of costly drug development. 

    Using traditional research methods, finding an appropriate drug candidate takes 4 to 5 years before the first clinical trial. This year, German biotechnology company Evotec announced a phase one clinical trial on a new anticancer molecule discovered in just eight months using AI techniques. 

    Personalized care

    By analyzing thousands of EHR datasets, AI can help identify high-risk patients and predict their current or future healthcare needs. AI-based chatbots in healthcare can also save time by providing both patients and doctors with accurate information faster — for example, by reminding patients to take medicine, recording symptoms, and suggesting appropriate appointment times.

    A US-based company, Actium Health, has already harnessed the power of AI to uncover hidden correlations in a patient’s data to identify and accurately predict their health risks. Solutions like these can help healthcare organizations shift their focus from treatment to prevention.

    Administrative applications

    Over one-third of all healthcare spending in the US goes to healthcare administration and billing. Many aspects of these tasks perfectly suit AI pattern recognition. Change Healthcare’s health intelligence platform is an excellent example of how embedding AI algorithms into clinical management solutions can help optimize claims processing, improve payment accuracy, and reduce costs.

    Deep learning algorithms also have the power to identify medical insurance fraud claims, as they can uncover patterns that deviate from expected behavior within datasets such as claims history, hospital-related information, and patient attributes.

    These are just a few examples of the power and potential of AI in the medical field. The range of areas where AI can bring benefits is huge, making AI highly attractive if you’re a software vendor looking to develop healthcare solutions.

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      Stepping into the future of healthcare

      As we’ve already shown, AI in healthcare is ready to boom.

      As well as accelerating healthcare processes, growing AI adoption looks set to create considerable cost savings for the industry. Accenture suggests that using AI applications could cut up to $150 billion of annual healthcare costs.

      AI can also help diagnose specific diseases and reduce error, subjectivity, and variability in diagnostic methods. What’s more, active AI usage is likely to transform the role of healthcare providers by freeing them from mundane administrative tasks. 

      In summary, AI can make good use of the big data healthcare has in spades. Yet, data isn’t the only thing that AI for healthcare projects requires. According to an IDC study, the biggest roadblock to adoption is a lack of qualified AI specialists.

      This is where we can help. With years of experience in AI development, Postindustria offers support at all levels, whether you’re looking to prepare your data and train algorithms or develop custom AI/ML solutions from scratch. Contact us today, and let’s talk about how you can make your business thrive with AI services.

      Got interested in how disruptive technologies are transforming industries? Check out Postindustria’s paper on augmented reality solutions for business below.

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