Machine Learning in Healthcare Industry: The “What,” “Why,” and “How”

We have all seen hospitals overwhelmed with patient surges, where doctors struggle to keep up, and critical data gets lost in the chaos. Due to these issues, misdiagnosis and delays are common, with a 2023 report that emphasizes medical errors as a leading cause of over 2.6 million deaths annually. This sounds distressful, but what if there was a solution? Of course, there is, and this is machine learning in healthcare industry.

With machine learning (ML), doctors can foresee diseases before they escalate, or hospitals can operate with such efficiency that waiting times are significantly reduced. This isn’t just a futuristic idea—several ML software platforms are already in clinical use. In 2018, IDX-DR became the first machine-learning system approved by the FDA for detecting diabetic retinopathy. Babylon’s chatbot triage system, in collaboration with the U.K.’s NHS and Viz.ai’s technology, now implemented in over 100 U.S. hospitals, are prime AI in healthcare examples. 

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But before we get into the details of ML’s impact on healthcare, let’s start with a basic definition and explanation. Whether you aim to improve patient care or optimize healthcare operations, mastering ML concepts with our expert-level ML/AI software development company is essential.

What is Machine Learning in Healthcare: Definition and Key Concept

To put it plainly, machine learning is part of AI, which is related to systems that learn by using data. Unlike manually coded traditional programming, ML functions automatically. It uses input and output data to make future predictions, while traditional programming uses input data to generate an output and runs a specific task as per the written code. This powerful automation and the ability to learn and predict independently are why ML has become more dominant than traditional programming in the tech industry.

Machine learning comes in three major types: 

1. Supervised Learning: This type requires training with a labeled dataset so that each input matches the correct output. It’s like learning with a teacher. For instance, an algorithm could be trained to diagnose diseases based on historical patient data and outcomes.
2. Unsupervised Learning: Unlike the previous type, which uses labeled data, unsupervised learning works with data that isn’t labeled. It’s like inspecting a new city without a map, uncovering hidden patterns and relationships along the way. In healthcare, unsupervised learning can help identify unknown disease patterns or patient segments.
3. Reinforcement Learning: This kind of ML has an agent that learns through interaction with its environment, receiving rewards or penalties. It’s like learning through trial and error. In healthcare, reinforcement learning can optimize treatment plans by continuously learning from patient responses.

Machine Learning Algorithms and Their Applications

Administrative, financial, operational, and clinical sectors are set to benefit greatly from AI and ML. The primary goal of machine learning in healthcare is to improve patient care and generate previously out of reach medical insights. It serves as a tool to support and validate doctors’ decisions through predictive algorithms. For instance, if a doctor prescribes a specific medication, ML can support this choice by identifying similar patients who have benefited from the same treatment. 

There are several machine learning algorithms that are widely used in healthcare, and each brings unique benefits and serves various purposes.

Logistic Regression

Logistic regression is essential for binary classification, where outcomes must be either positive or negative. In healthcare, it’s used to diagnose diseases and find specific conditions. It’s also widely used in fraud detection. 

Key applications include: 

• Assess the risk of disease development based on certain factors.
• Differentiate between patients with and without a specific condition.
• Examine risk factors and associations with diseases in epidemiological studies.

Decision Trees

Decision trees are appreciated for their straightforward and easy-to-interpret nature. Perfect for both classification and regression, they handle both numerical and categorical data smoothly. 

Key applications include:

• Diagnosis based on patient symptoms and history.
• Suggestions for the best treatment options for individual patients.
• Identification of patients at high risk for complications or negative outcomes.

Random Forest

Random forest, by leveraging multiple decision trees, achieves notable robustness and accuracy. It handles complex, high-dimensional datasets with noisy or missed data very well, which is crucial in healthcare, where datasets are frequently incomplete.

Key applications include:

• Assess the likelihood of disease onset by evaluating patient characteristics and risk factors.
• Determine patient outcomes, like the chances of recovery or the risk of death.
• Identify which variables or risk factors have the most influence through feature importance analysis.

Support Vector Machines

Support Vector Machines are strong classifiers, particularly effective with complex, high-dimensional data. In healthcare, they play a crucial role in tasks that require precise classification, such as disease diagnosis and patient risk stratification.

Key applications include:

• Diseases classification, which includes identification of different types or stages of cancer based on medical images or genomic data.
• Sorting patients into risk groups based on clinical variables or biomarkers.
• Analysis of medical images to segment and identify tumors in radiological scans.

Neural Networks

Neural networks, particularly deep learning models such as CNNs and RNNs, can understand complex patterns and relationships within large datasets. Their versatility applies to numerous healthcare domains, from medical images analysis to clinical decisions support and genomics study.

Key applications include:

• Medical image assessment to identify irregularities in X-rays, MRIs, or histopathological slides.
• Clinical decision-making support for risk prediction, treatment recommendations, and patient monitoring.
• Time-series data (like electronic medical records) analysis to forecast patient outcomes and identify anomalies.

K-Nearest Neighbors (KNN)

Though simple, the K-Nearest Neighbors algorithm is very effective for classification and regression tasks. It works particularly well with small to medium datasets and easily handles numerical and categorical data points. KNN is often preferred for its ability to detect anomalies.

Key applications include:

• Classify diseases, like forecasting the likelihood of a particular condition by comparing them to similar cases in the dataset.
• Patient similarity identification to pinpoint individuals with comparable medical histories for tailored treatment plans and advice.
• Anomalies detection in patient vital signs or laboratory results.

How Is Machine Learning Used in Healthcare?

Need to say that AI encompasses many technologies, with ML as a central component. In healthcare, AI applications go beyond predictive analytics and diagnosis. They include natural language processing to understand patient records, robotics for surgery, and chatbots to engage and support patients. Let’s discover these key machine learning applications in healthcare: 

Robot-Assisted Surgery

ML algorithms are being integrated into robotic surgery systems to improve precision and minimize errors in minimally invasive procedures. Companies like Intuitive Surgical develop robotic surgical systems with AI capabilities to assist surgeons. That is one of machine learning in healthcare examples that showcases the potential benefits of machine learning and healthcare integration.

Drug Discovery and Development

Traditionally, new drug development spans 10 to 15 years, from the discovery of a compound to its commercialization, with costs averaging $1-2 billion for R&D and clinical trials. Sadly, about 90% of clinical drug development efforts end in failure.

To address these challenges, many pharmaceutical companies integrate ML into their drug discovery processes to expedite the journey of new therapeutics from the lab to the clinic. For instance, BenevolentAI employs algorithms to uncover promised drugs for several diseases, which demonstrates a key AI in healthcare examples.

Early Disease Detection and Diagnosis

Diagnosing diseases accurately is a significant challenge due to the variable mechanisms of diseases and the diverse ways symptoms present in patients. Nearly 6% of emergency department visitors in the US receive incorrect diagnoses. Diagnostic errors account for approximately 75% of serious incidents in cases of vascular events, infections, and cancers.

Studies have shown that ML can enhance disease detection and diagnosis by identifying patterns indicative of illnesses. Its algorithms can analyze medical images, like mammograms or X-rays, to detect signs of cancer at earlier stages. This allows for earlier intervention and potentially better patient outcomes.  

In a study by Paige.AI, their ML model identified signs of breast cancer in mammograms with high accuracy, exceeding the performance of some radiologists. AI in healthcare examples like these highlight the transformative potential of machine learning. Thanks to the benefits of machine learning in healthcare, the US FDA has already approved several imaging medical devices through ML.

Here is a table with possible usage of machine learning in healthcare industry:

The Impact of Machine Learning on Healthcare

So, it’s time to summarize the impact of machine learning applications in healthcare. Of course, we can’t cover it all in one article, but won’t miss the main benefits.

Enhancing Diagnostic Accuracy

Machine learning reshapes how doctors diagnose diseases, especially in areas like radiology and pathology. Doctors who examine an MRI or CT scan may miss important details. However, with ML, the computer helps them spot issues like tiny tumors or early signs of disease that are unavailable to the human eye.

According to a study published in the Journal of the American Medical Association, AI systems can diagnose conditions from medical imaging with an accuracy rate of up to 94.5%, which is comparable to that of experienced radiologists. This means doctors can make more accurate diagnoses faster, catch diseases earlier, and start treatment sooner.

Personalized Medicine

Personalized medicine has become a reality thanks to machine learning. Doctors can now look at a patient’s genetic information and use that data to figure out the best treatment plan. This means treatments can be tailored specifically to the individual, which makes them more effective and reduces the risk of side effects. It’s like having a custom-made solution for each patient’s unique health needs, which could significantly improve health outcomes.

Operational Efficiency and Better Patient Care

Machine learning also helps hospitals and clinics operate more efficiently. Consider the amount of time spent on paperwork and administrative tasks. With ML, many of them can be automated. For instance, computer systems can handle scheduling, billing, and record-keeping more effectively. Hospitals that use ML algorithms for scheduling have reported a 25% decrease in appointment no-shows.

That means healthcare professionals can spend more time with patients and less on paperwork, which leads to a smoother-running hospital and a better patient experience. Indeed, a study found that hospitals that implemented ML in their operations saw a 20% increase in patient satisfaction scores.

Future Trends and Innovations of ML in Healthcare

Emerging technologies like Tiny ML, big language models, and low- and no-code solutions are all expected to play significant roles soon.

Tiny ML for IoT (Internet of Things)

With Tiny ML, small, low-power devices such as microcontrollers and sensors handle machine-learning tasks. That allows real-time data processing and decisions at the edge, which minimizes the reliance on cloud connectivity. It ensures quicker, more efficient operations in areas like wearable health monitors and smart home devices. 

The Big Model Creation

Imagine a single, all-purpose ML model that handles various tasks simultaneously. This future model could be trained across multiple domains, which makes it versatile enough to diagnose cancer, classify medical images, and more. Such advancements would streamline healthcare operations and improve patient outcomes. The development of powerful quantum processors could further enhance these capabilities, which makes this an exciting area to watch.

Distributed ML Portability

As healthcare data grows, the need for flexible, distributed ML systems becomes more critical. Future advancements will allow scientists to integrate their work seamlessly across different platforms without reinventing algorithms for each system. That would mean more efficient data analysis and application in healthcare, which leads to better patient care and streamlined operations.

No-Code Environment 

Low- and no-code platforms enable users to create machine-learning models and applications without extensive programming knowledge. These solutions use visual interfaces and pre-built components to simplify the development process and make it accessible to a broader range of users. This democratization of technology allows more organizations to leverage ML for their specific needs, which speeds up innovation and deployment.

Related – How to create an AI 

ML and Genomics Convergence

ML combination with genomics is set to revolutionize personalized medicine. Analyzing large amounts of genetic data, ML can identify genetic markers for diseases, predict individual responses to treatments, and develop customized therapeutic strategies. This integration will allow healthcare providers to offer more precise and effective care based on each patient’s unique genetic profile.

Big Data in Healthcare

Big data becomes increasingly crucial in healthcare, providing a foundation for machine learning applications. Through the collection and analysis of extensive health data from various sources, healthcare providers can gain insights into population health trends, treatment outcomes, and patient care. ML algorithms process this data to uncover patterns and correlations, guide clinical decisions, improve operational efficiency, and drive innovations in medical research and public health.

Barriers to Machine Learning Adoption in Healthcare

Technologies as complex as machine learning are not easy to implement. Here are some key hurdles and tips from our experts on how to overcome them:

Data Quality

The first crucial step is to ensure the quality of the data used by ML systems. Simply using any available data isn’t sufficient; the data must be of the highest quality and uniformity.

Expert Tip: Avoid errors and inconsistencies, as poor-quality datasets lead to inaccurate and unreliable predictions. Ensure the integrity and consistency of the data for ML systems to deliver optimal results.

Patient Consent

ML technology can improve medical treatments, but it requires explicit patient consent to use their data. Without it, healthcare providers risk violating patients’ privacy and confidentiality rights. 

Expert Tip: Follow regulations that govern personal data use, ensuring patients’ consent for ML purposes. Anonymize and secure patient data and create clear policies for data management and access requests.

Safety and Transparency

Safety and transparency are major challenges in implementation of machine learning algorithms in healthcare. They are fairly complicated, and how they come to conclusions can be pretty opaque.

Expert Tip: Design ML models with transparency in mind and ensure healthcare professionals understand how these algorithms make decisions. Establish clear guidelines and regulations for ML development and usage to ensure safety and reliability.

Data Privacy

Data privacy is paramount in healthcare because medical data contains sensitive patient information. Despite regulatory and governmental efforts to protect this data, no system is entirely immune to cyber threats.

Expert Tip: Implement robust cybersecurity measures and work closely with stakeholders to develop and enforce guidelines for the secure implementation of ML technologies.

Unstructured Data and Interoperability

One significant challenge of machine learning in healthcare industry is dealing with healthcare data. Since medical data is frequently unstructured, ML algorithms can have difficulties processing it.

Expert Tip: Find effective ways to structure and organize this data, which makes it ready for ML applications. Ensure standardization across all systems within your organization to facilitate integration and interoperability.

Shortage of Machine Learning Experts

The demand for ML experts has increased nearly tenfold in recent years, with many professionals now employed by major global tech corporations. This shortage can slow the progress and implementation of ML technologies.

Expert Tip: Consider investments in training to enhance your team’s expertise, or partner with external specialists, like the healthcare software developers at Relevant, to close any skills gaps.

Machine Learning in Healthcare Industry: Final Thoughts

In 2023, the healthcare AI market was valued at $14.5 billion, and it’s projected to soar to $20.9 billion by 2024 and a staggering $148.4 billion by 2029. According to a Harvard Business Review report, AI-driven technologies like machine learning are expected to save the US healthcare sector $150 billion annually by 2026.

So, the future of machine learning in healthcare industry is very bright. It is clearly moving towards highly technological products. To leverage these opportunities effectively, you need a partner with our qualifications and experience. So, where should you look to hire AI engineers? 

Of course, here, at Relevant. As an outsourcing development company, we are dedicated to helping you harness the power of AI and ML. We build custom:

• Medical Speech Recognition Software: AI programs that include medical speech-to-text recognition tools to assist with various clinical tasks.
• Medical Chatbots: AI-powered solutions to assist with front-desk support, appointment scheduling, patient data collection, mental health support, and more.
• Healthcare Analytics Software: Powerful tools for EHR, pharmaceutical research, real-world data processing, intelligent clinical decision support systems, and more.
• Text Recognition Solutions: OCR software to digitize medical records, lab tests, imaging reports, and more, which allows easy transfer and storage in the EMR.
• Augmented Intelligence in Healthcare: Predictive analytics solutions to enhance the planning and management of clinical and administrative workflows.
• Medical Image Recognition Solutions: Computer vision solutions to help recognize and classify specific conditions in DICOM images, which aid in accurate diagnosis and treatment.

Whether your goal is to fine-tune diagnostic methods, streamline your hospital operations, or create personalized treatment strategies, we have the expertise and tools to assist you. Contact us to create smarter healthcare systems for everyone.