Use Cases and Advantages of ML in Healthcare
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Machine Learning in Healthcare: Use Cases & Benefits

22 Jun 2022

The healthcare sector has been the front runner in adopting digital transformation across the board. Right now, machine learning (ML), a subset of artificial intelligence, is playing a key role to address health-related areas. This would include having the ability to extract, share & leverage health data and records, development of new medical procedures & even the treatment of chronic diseases. From enhancing operations at a lower cost to improving care quality, ML is revolutionizing every aspect of healthcare with limited human intervention!

And, with the amount of data generated for each patient, ML algorithms in healthcare certainly have great potential. Therefore, it is not surprising why we’re witnessing multiple successful ML applications in healthcare right now. In this article, we will explore the role of machine learning in healthcare with its real-world applications and advantages. Let’s begin!

Table of Content:

The Importance of Machine Learning in Healthcare

With the healthcare industry moving towards value-based care, creating a system that is focused on lowering costs seems counterintuitive. However, this two-pronged goal is achievable when healthcare organizations have end-to-end visibility into clinical quality measures and the expenses associated with it. This is where machine learning can be leveraged to accelerate routine workflows, data management, drug development, diagnosis, treatment & regulatory activities in healthcare. Here’s how!

  • Recommendations – ML algorithms can extract and deliver vital medical information without needing you to actively search for it
  • Classification – Helps identify and label the kind of medical case or condition a patient is dealing with
  • Prediction – Using past and current data coupled with common trends, smart algorithms can make an accurate prognosis on how the future developments & events will unfold
  • Clustering – Can be leveraged to club similar medical cases together for analyzing patterns and conducting research
  • Ranking – Helps extract the most relevant information first, making the search fast and easy
  • Detecting Anomalies – Enables easy identification of specimens that stand out from common patterns for timely intervention
  • Automation – Can put standard, repetitive clinical operations such as appointment scheduling, inventory management, and data entry on the autopilot mode

Real-World Applications of Machine Learning in Healthcare

Real-World Healthcare Machine Learning Use Cases

Below are some of the prominent machine learning use cases in healthcare that you can consider while assessing the potential of this technology.

  • Making Diagnoses Via Medical Image Analysis: Microsoft’s project InnerEye harnesses the potential of ML to make a distinction between healthy anatomy and tumors. It uses 3D radiological images to assist medical specialists in surgical planning and radiotherapy, among other forms of treatment.
  • Leveraging Crowdsourced Medical Data: Today, clinical researchers have access to a massive volume of patient data from different sources that can be leveraged for improving the diagnosis process & issuance of prescriptions in the future. StuffThatWorks is an AI-powered community that supports human decision-making by offering its members easy access to lesser-known treatment alternatives for more than 500 medical conditions such as perioral dermatitis, osteoarthritis, and fibromyalgia.
  • Clinical Research: Implementing ML-based predictive research on latent clinical trials can supply a wide range of data pointers backed by the best sample size to reduce data-based errors. The researchers of Mayo Clinic leverage the Sepsis Sniffer Algorithm that uses vital sign measurements and patient demographic data to trigger an alert when there is a heightened risk of developing sepsis. This cuts the manual screening significantly while enabling doctors to spend more time treating the patient and improving the outcome.
  • Infectious Diseases Outbreak Prediction: Machine learning is also being implemented to monitor and predict global pandemics. Artificial neural networks are making it easy to collect a massive amount of data in real time from social media platforms, website information, and satellites to predict the outbreak of chronic infectious diseases. This technology is highly beneficial for third-world countries that lack vital medical infrastructure.
  • Clinical Decision Support System (CDSS): The said tools are designed to analyze a large amount of healthcare data to determine an underlying disease, recommend the next stage of treatment, highlight potential health problems, and enhance the efficacy of patient care. Effectively used by CDSS, it enables physicians to do their job quickly and efficiently while greatly reducing the possibility of the wrong diagnosis or recommending an ineffective treatment.
  • Patient Risk Identification: Across the globe, healthcare organizations are using tools built on ML models that use anomaly detection algorithms to accurately forecast the risk of sepsis, strokes, heart attacks, and other serious health implications. These tools use patient health records, their measurements of vital signs, and real-time health assessments to alert the medical staff of imminent dangers and drive preventive action. KenSci uses machine learning technology to predict illness and population health risks to help physicians intervene on time and improve patient outcomes.
  • NLP For Automating Administrative Tasks: A major chunk of administrative tasks in the healthcare industry involves reviewing and updating health records. This is where natural language processing tools can help offload tedious and time-consuming manual tasks. NLP tools use algorithms to determine and classify words and phrases to assist doctors with reviewing medical charts and summaries. Doctors can also dictate notes to the EHR system with ease using NLP technology.

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Benefits Of Machine Learning in Healthcare

Using ML in healthcare operations can prove to be extremely beneficial to organizations. While a healthcare professional and a machine learning algorithm are most likely to achieve the same conclusion based on the same data sets, the use of ML ensures getting results much faster & allowing to start the treatment earlier.

Listed below are key benefits of leveraging ML for the healthcare sector;

  • Automated Health Record Digitization: Maintaining accurate and up-to-date medical records manually is downright tedious and exhausting. Technology has greatly eased the data entry process to make it faster, effortless, and more reliable. Data classification methods that use optical character recognition (OCR) techniques are making it mainstream such as MATLAB’s ML-based handwriting recognition technology.
  • Improved Quality and Speed of Diagnosis: There are a lot of life-threatening diseases that require early-stage detection. The combination of supervised and unsupervised algorithms in ML would certainly prove to be highly beneficial. Further, a comparison of historical data with the patient’s current health statistics would help identify the symptoms of a particular condition and even raise a red flag. This would prompt medical practitioners to take corrective action on time.
  • Detecting Frauds: According to the National Healthcare Anti-Fraud Association, 3% of healthcare claims are fraudulent and their cost is about $68 billion This converts to $2.26 trillion in healthcare spending for the U.S. Using ML models, healthcare organizations can detect anomalies well in time to prevent such fraudulent claims before they are paid up.
  • Personalized Treatment: Treatments can only be made effective when they pair patient health records with predictive analytics. Currently, physicians and caregivers are restricted to choosing from a certain set of diagnoses based on a patient’s genetic data and symptomatic history. But machine learning can help generate multiple treatment options using sophisticated health measurement capabilities.
  • Streamlining Diagnostics: Specialized areas of medicine involve the recognition of specific patterns such as pathology, radiology, and dermatology. These disciplines have witnessed increased adoption of ML models that are trained to scan medical images and identify abnormalities. This helps enhance the pace of diagnostic procedures with accuracy to achieve the ultimate goal of improving patient care.
    While Machine Learning has a lot of untapped potential for the sector, it is not without challenges like heterogeneous data, inaccurate health records, disparate data systems & supportive software. Not to mention – there exist specific ethical concerns surrounding data security, confidentiality, patient safety, and accountability. So, if you want to benefit from implementing ML in your organization, be sure to consult a healthcare IT specialist who can offer a solid blend of development experience and technical expertise in building smart healthcare solutions.

The Most Commonly Used Machine Learning Algorithms in Healthcare

The following ML algorithms are transforming the healthcare landscape by efficiently addressing various medical challenges:

Artificial Neural Network (ANN):

Imagine a Machine Learning algorithm that behaves a bit like the human brain. That’s ANN for you. These networks sift through data with minimal human intervention, making them perfect for healthcare applications like medical imaging in radiology and recognizing text and speech.

Logistic Regression:

When it comes to predicting binary outcomes, logistic regression is the most popular and preferred ML algorithm. It’s not only easy to implement but also reveals the significance of each variable in the final prediction. Healthcare professionals can rely on it for tasks such as medical diagnosis, identifying at-risk patients, and tailoring behavior plans.

Support Vector Machines (SVMs):

SVMs are ideally used for addressing classification challenges that require you to classify data with missing values from incomplete datasets.
To put it simply, the further the data points appear from the Y-axis on the graph below, the higher the possibility that they belong to the respective classes. In healthcare, SVMs find their place in drug development, predicting medication adherence, and segmenting images and text.

How Rishabh Can Help You as a Partner for Digital Health

You can leverage our custom healthcare software development experience to build patient-centric digital solutions and improve operational efficiency & treatment outcomes. We leverage the latest in technology to help you develop and implement a wide range of solutions including but not limited to:

Success Story: Next-Gen ML-Powered Healthcare Monitoring System

A UK-based medical service provider wanted to improve their clinical efficiency and patient care. They turned to Rishabh for creating a system that offers real-time human activity recognition within their existing clinical systems. This would enable the client to provide better diagnosis & treatment for critical care patients.


  • No system in place to organize and analyze patient data
  • Inability to assist critical care patients in real-time
  • A time-consuming process for understanding, diagnosing and projecting the next course of treatment due to diverse device functionality


We created ML-based data models for all their connected devices for real-time recording and analysis of patient health. This AI-powered patient monitoring system enabled continuous monitoring of patients and centralized health management.

Benefits Delivered

  • Real-time data collection & patient-intelligence
  • 63% accurate diagnosis
  • 5x improvement in operational efficiency

Final Thoughts!

Machine learning is all set to be an indispensable technology to the sustainable success of the healthcare sector. Hence, healthcare professionals and clinicians must start utilizing this technology to work in their favor. It is high time they start leveraging the algorithms to optimize their data-driven tasks, risk scoring, clinical decision support & early alert mechanism as some of the significant areas of development. And, to do that there’s a clear need for these organizations to leverage the next-gen data analytics to stay competitive.

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