The adoption of machine learning in healthcare is growing, with an expanding list of use cases ranging from medical imaging analysis to drug discovery. Grand View Research estimates that the global AI in the medical domain market was valued at $22.45 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 36.4% from 2024 to 2030. Additionally, a report by Meticulous Research projects that global machine learning in the healthcare market will reach $19.25 billion by 2027, with a CAGR of 44.9% from 2020 to 2027.
Machine learning tech is revolutionizing disease discovery and treatment methods, inspiring healthcare professionals to push the boundaries of what is possible. Communities now expect their medical facilities to deliver top-notch services, including advanced medical treatments, surgical procedures, and proactive disease forecasting. That makes machine learning the key to meeting these high expectations. But how exactly is ML utilized in healthcare? What are the tech’s most promising use cases? Let’s find out!
The Key Machine Learning Algorithms for the Medical Domain
Below are the most common algorithms typically used in ML healthcare applications.
Logistic Regression
Logistic regression is widely utilized for binary classification tasks, determining whether an outcome is positive or negative. In healthcare, it aids in diagnosing diseases and determining the presence of specific conditions due to its simplicity, interpretability, and efficiency in modeling probability. Another notable use of this algorithm is in fraud detection.
Tasks facilitated by logistic regression involve:
- Predicting disease likelihood based on risk factors
- Differentiating between patients with and without certain conditions
- Analyzing risk factors and disease associations in epidemiological clinical trials
Decision Trees
Decision trees are favored for their clarity, making them suitable for classification and regression tasks. They handle numerical and categorical data, making them ideal in clinical settings where you must understand the reasoning behind diagnosis or treatment recommendations.
Tasks facilitated by decision trees involve:
- Identifying likely diagnoses based on patient symptoms and history
- Recommending treatment options based on patient characteristics
- Recognizing patients at high risk of complications or adverse outcomes
Random Forest
Random forest, a comprehensive learning method, comprises multiple decision trees and is known for its robustness and accuracy. It effectively handles complex datasets with high dimensionality, noise, or missing data, making it particularly valuable in healthcare.
Tasks facilitated by random forest involve:
- Predicting disease onset based on patient characteristics and risk factors
- Forecasting patient outcomes, such as recovery likelihood or mortality
- Conducting feature importance analysis to identify influential variables or risk factors
Support Vector Machines (SVMs)
SVMs are robust classifiers, especially effective with high-dimensional data and nonlinear relationships. They are extensively used in healthcare for tasks requiring precise classification, such as disease diagnosis and patient risk stratification.
Tasks facilitated by SVMs involve:
- Classifying diseases, for example, differentiating between cancer types or stages based on imaging or genomic data
- Grouping patients into risk categories based on clinical variables or biomarkers
- Analyzing medical images like segmenting and classifying tumors in radiological images
Neural Networks
Neural networks, profound learning algorithms like CNNs and RNNs, excel at learning complex patterns and relationships from large data volumes. They are highly versatile and applicable across various healthcare domains, from medical image analysis to clinical decision support.
Tasks facilitated by neural networks involve:
- Analyzing medical images to detect abnormalities in X-rays, MRIs, or histopathological slides
- Supporting clinical decisions for risk prediction, treatment recommendations, and patient monitoring
- Analyzing time-series data, such as electronic medical records, to predict patient outcomes and detect anomalies
K-Nearest Neighbors (KNN)
K-Nearest Neighbors (KNN) is a straightforward yet effective algorithm for classification and regression tasks. It is beneficial with small — to medium-sized datasets. KNN handles numerical and categorical data points efficiently and is famous for anomaly detection.
Tasks facilitated by KNN involve:
- Classifying diseases by predicting the likelihood of a patient having a particular condition based on similar cases in the dataset
- Analyzing patient similarity to identify individuals with similar medical profiles for personalized treatment plans and recommendations
- Detecting anomalies by identifying outliers in patient vital signs or laboratory results
Each ML algorithm has unique strengths and applications within the healthcare sector. They contribute to more efficient and accurate diagnoses, treatment plans, and patient care. These algorithms will become crucial in transforming care delivery and outcomes as ML evolves.
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7 Advantages of Adopting Machine Learning in Healthcare
Implementing AI in the medical domain has shown a tremendous impact, allowing physicians to dedicate 17% more time to patients, according to Statista. This increase in efficiency highlights the numerous advantages that machine learning brings to healthcare organizations:
- Daily Tasks Automation. ML streamlines processes like EHR management and virtual nursing, automating many repetitive tasks. That reduces operational costs and frees healthcare professionals to focus on more critical patient care activities.
- Decision-Making Enhancements. By identifying patterns in vast healthcare datasets, machine learning aids clinicians in diagnostics and decision-making. That leads to improved patient outcomes, enhancing the technology’s credibility in healthcare.
- Better Patient Experience. Chatbots and virtual assistants improve patient experience by providing easier access to essential healthcare services. That makes interactions more efficient and responsive.
- Innovations Leveraging. Machine learning optimizes drug discovery and development, helping pharmaceutical companies reduce time-to-market and lower research costs.
- Broad Access to Healthcare Services. Machine learning’s efficiencies enable healthcare organizations to serve more patients without compromising the quality of care. This extended access makes medical professionals feel more inclusive and accommodating in their services.
- Lower Risks. Early detection of serious illnesses minimizes risks during robot-assisted surgeries and facilitates quick identification of high-risk patients. That enhances safety and reassures healthcare professionals and patients.
- Efficient Data Management. ML algorithms help healthcare organizations identify and correct inconsistencies in disparate datasets, ensuring data integrity and reliability.
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Top Machine Learning Applications in Healthcare
Numerous machine learning applications exist in healthcare, including patient apps, e-triage tools, online symptom checkers, virtual agents, and bionic pancreases for diabetic patients. Below, you can explore famous ML use cases and real-world examples to understand the tech’s potential value for medical practices.
Predicting Disease Outbreaks
Satellites can collect vast amounts of real-time and historical data. Predictive analytics tools aggregate this information to forecast potential disease outbreaks. For instance, you can predict malaria outbreaks by analyzing monthly rainfall, temperature, and other parameters. That is particularly useful in developing countries that lack medical infrastructure and education to combat such diseases. Knowing about these outbreaks in advance allows governments to take preventive measures, minimizing the impact and saving lives.
Example: ProMED
ProMED (Program for Monitoring Emerging Diseases) provides an online, real-time information analysis and reporting system that shows infectious disease outbreaks worldwide. Moreover, it represents any exposure to toxins affecting human or animal health. ProMED aggregates data from various sources, including official, media, and subscriber reports. An expert team reviews these reports before acceptance into the system. The data provided by ProMED is visualized on HealthMap, showing disease outbreaks in each country.
Modifying Patient Behaviors
Many prevalent diseases are manageable or even avoidable. For example, type 2 diabetes, obesity, and heart disease can sometimes be detected early or avoided with a healthier lifestyle. However, lifestyle changes require constant reminders and follow-ups. Machine learning algorithms can aggregate health data from patients’ connected medical devices and sensors. That helps generate insights into their behavior and guide them on the transformational journey.
Example: SmokeBeat
SmokeBeat is a smoking cessation app that passively gathers data on the user’s smoking behavior. It uses an accelerometer on a smartwatch or smart band to detect hand-to-mouth gestures. SmokeBeat processes this data and offers incentives for real-time cognitive behavior therapy. User responses to these incentives are measured and recorded to improve effectiveness. Importantly, SmokeBeat fosters a supportive social network by comparing users’ smoking data with their peers of choice, creating a sense of encouragement and solidarity.
Promoting Virtual Nursing
Healthcare facilities employ virtual nurses to manage a wide range of healthcare tasks. They include appointment scheduling and reminders, medication and chronic disease management, post-discharge care, health alerts, and emergency response. These computer-generated avatars, designed to be social, empathetic, and informative, are available round the clock. Such a complex approach ensures that patients can get their questions answered and concerns addressed at any time, even between doctor visits, providing a thorough and effective service.
Example: Molly
Molly, a virtual nurse, proves the convenience of this innovative approach. This female avatar can remotely monitor medical conditions by receiving data like blood pressure and weight from patients’ monitoring devices connected via Bluetooth. These devices are placed in patients’ homes, making it convenient to take measurements as often as needed. Molly can recognize speech and verbally answer patients’ queries, and it offers a chatbot for private discussions.
Analyzing Medical Images
Despite advancements in healthcare tech and data science, medical image analysis remains a meticulous and error-prone task. Such a rigorous nature underscores the importance of attention to detail in this field. ML enhances this process by identifying subtle changes in X-ray, CT, or MRI scans, aiding radiologists in detecting and diagnosing diseases more accurately.
Example: SubtleMR
SubtleMR, developed by Subtle Medical, is a machine learning-based software that enhances MRI image quality. By utilizing denoising and resolution enhancement techniques, SubtleMR improves the sharpness of images from an MRI scanner. For instance, RadNet, a promising outpatient imaging provider in the USA, reported a 33-45% acceleration in their protocols after adopting SubtleMR technology.
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Identifying Patients at High-Risk Conditions
Inaccurate or incomplete diagnoses can severely impact patient outcomes and, in extreme cases, lead to fatalities. Many companies are leveraging machine learning to enhance medical diagnostics and address this critical issue. ML-powered pattern recognition and automation also enable clinicians to identify high-risk patients more quickly.
The potential of machine learning to identify high-risk patients is a hopeful sign for the future of patient care. ML-based systems can process PHI (Protected Health Information) and categorize patients into risk groups based on detected risk levels.
Example: Face2Gene
Face2Gene is a precision medicine app that employs ML-enabled facial recognition technology to assist clinicians in more accurately diagnosing rare diseases. With machine learning, the app can detect phenotypes, identify relevant facial features, and assess the likelihood of a patient having a particular syndrome.
Performing Surgeries with Robots
Robots in healthcare are not new, but ML-powered robotic surgery assistants are transforming the field. These robots enhance precision, access different areas of the body with minimal intervention, and shorten operation time. Such a relief from the burden on human surgeons is a promising sign for the future of care delivery. They are precious in performing complex surgeries, reducing patient trauma and recovery time, and are indispensable in microsurgery.
Example: Senhance
Senhance is a console-based, multi-armed surgical system that surgeons control remotely. It relies heavily on ML and deep learning models to realize complex healthcare procedures. A machine learning-driven database allows surgeons to undergo simulation training during the preoperative stage. Meanwhile, during surgeries, the system’s Intelligent Surgical Unit uses data from an eye-tracking camera to adjust the camera view, predict when a surgeon needs to zoom in and enhance images in real time.
Improving Drug Discovery
Drug discovery is costly and time-consuming, requiring researchers to test thousands of elements and their combinations before finding a viable drug. Machine learning algorithms streamline this process in several ways:
- Reducing clinical trial duration by predicting drug performance
- Identifying new treatments by combining existing drugs
- Developing new drugs through compound testing
- Finding new applications for previously tested substances
Example: AlphaFold
AlphaFold, a machine learning-powered system developed by Google’s DeepMind, automatically predicts protein structures. This breakthrough in biology accelerates drug screening and development by predicting protein interactions. In 2021, Google launched Isomorphic Labs to leverage AlphaFold’s technology to find cures for prevalent diseases.
Optimizing Hospital Management
As the demand for healthcare services increases, hospitals’ management systems become more complex and chaotic. Adopting ML-based systems streamlines administrative processes, leading to enhanced executive functions. Such systems can improve staff scheduling, optimize supply chain and inventory management, facilitate resource allocation, and streamline medical records management.
Example: Globus.ai
Norway-based Globus.ai has developed a system that helps medical institutions streamline staffing. Using natural language processing and machine learning, the system matches healthcare employees to specific tasks based on their skill sets, making task scheduling more efficient. The system also considers legal requirements, such as limits on working hours or the need for specific expertise during certain procedures.
Health Insurance
Health insurance is crucial for making healthcare accessible, yet many processes remain manual and inefficient. Machine learning can improve these operations by:
- Assisting in early fraud detection with pattern recognition algorithms
- Automating credit underwriting, risk assessment, claims processing, and client support
- Reducing the probability of false positives compared to rule-based fraud detection systems
Example: Maya Intelligence
Temple University Health System (TUHS) in Philadelphia partnered with Accolade, which provides the Maya Intelligence platform to help patients select appropriate healthcare coverage. The system uses machine learning to analyze medical claims, lab results, and other relevant patient information, offering tailored healthcare plans. This implementation saved TUHS over $2 million in healthcare claim costs and increased staff engagement by 50%.
10 Real-Life Companies Successfully Utilizing ML in Healthcare
After considering all the benefits and use cases of ML in healthcare data analysis, let’s examine some companies that effectively leverage tech in the medical domain.
Asimov
Asimov, a pioneering genetic design firm, is at the forefront of understanding genetic systems with its data-driven approach. By harnessing synthetic mammalian biology, Asimov creates tools to program living cells. The company’s unique blend of computer-aided design and ML results in computational models that pave the way for genetically engineered therapies.
Strive Health
Strive Health is significantly impacting kidney disease care with its technology and services. The CareMultiplier platform, powered by machine learning algorithms, provides crucial data insights. That drives personalized plans for treating and managing kidney conditions, leaving a lasting impression on the healthcare industry.
Definitive Healthcare
Definitive Healthcare provides commercial intelligence for various healthcare sectors, including medical device companies, providers, and biotech firms. Their analytics solution, Atlas AI, uses artificial intelligence, machine learning, data modeling, and other advanced techs. That helps convert large datasets into insights informing business decisions.
Cohere Health
Cohere Health develops technology to streamline utilization management for health plans. Their Cohere Unify Platform uses predictive solutions powered by AI and ML to create personalized, data-driven care plans. It automates steps across the prior authorization workflow, saving time.
GRAIL
GRAIL is dedicated to early cancer detection solutions, and a critical part of its success is the use of machine learning. By combining genomics expertise with this advanced technology, they have developed the Galleri test. This test identifies DNA fragments in blood samples and uses machine-learning algorithms to determine whether they are cancerous.
Novo Nordisk
Novo Nordisk, a pharmaceutical company, is committed to improving the lives of people with chronic diseases. Its Modelling and Predictive Techs department applies ML algorithms to automate complex human cognitive tasks, aiding in fast, reliable drug development.
Linus Health
Linus Health uses machine learning in healthcare to develop advanced screening tools for early detection of Alzheimer’s and other neurological conditions. They offer cognitive function assessments and monitor brain health changes, utilizing artificial intelligence to analyze data and deliver sophisticated diagnostic results.
Tebra
Tebra’s Kareo product provides a cloud-based clinical and business management platform to support independent practices. The platform facilitates patient health and financial data transfer, simplifying record management and transactions. Kareo also applies AI technology to automate repetitive tasks, reducing practitioners’ operational costs.
Ciox Health
Ciox Health powers its Datavant Switchboard platform with machine learning to give healthcare professionals faster access to patient data. Organizations can set personalized controls within the platform, allowing staff to request specific data types. Ciox Health’s technology ensures compliance with privacy rules, keeping electronic health records secure.
Subtle Medical
Subtle Medical leverages AI, machine learning, and deep learning to enhance medical imaging quality for radiologists. Their product, SubtleMR, effectively reduces image noise and focuses on critical areas like the head, neck, abdomen, and breast. The improved image clarity helps radiologists complete exams more efficiently, shortening patients’ time for care.
Our blog examines the critical benefits, everyday use cases, challenges, and real-life examples of Big Data adoption in healthcare. Discover the article now!
Challenges of Machine Learning Implementation in Healthcare
While the potential for machine learning in healthcare is vast and promising, you must address several critical challenges to build effective systems powered by this technology.
Complexity and Unstructured Information
Healthcare data comes in various formats, including text, images, and time-series information, which makes it complex and unstructured. This diversity can pose challenges in preprocessing and extracting meaningful insights from the medical data.
Scattered Information Sources
Patient information is often distributed across multiple systems, such as EHRs, medical devices, and administrative databases. Integrating data from these sources while ensuring consistency and accuracy is challenging.
Insufficient Data Quantity
Building accurate machine learning models requires large volumes of high-quality labeled data. However, privacy concerns often limit medical datasets, making it challenging to train robust models. These aspects are crucial in healthcare projects and must always be prioritized.
Sensitive Data Protection
Protecting sensitive patient information, including patient demographics, medical history, and treatment records, from unauthorized access, breaches, and misuse is critical. This ensures patient privacy and regulatory compliance, building a solid foundation for using ML in healthcare data analysis.
Compliance with Rules and Regulations
In the US, healthcare data is governed by stringent privacy regulations like HIPAA. Adhering to these rules while using information for machine learning applications depends on many security measures, data encryption, and access controls.
Disparate Health Systems
Healthcare organizations often use different systems and protocols. Integrating machine learning platforms with existing infrastructure and achieving interoperability between these disparate systems can be challenging without proper preparation.
Information Silos
Many organizations have data silos that contain valuable information for machine learning solutions. Breaking down these silos and establishing interoperable frameworks is essential to leverage machine learning in healthcare fully.
Lack of Strategy
Machine learning provides tangible long-term benefits when the entire organization supports its adoption. However, ML capabilities can impact traditional healthcare workflows more than other techs, requiring changes in usual processes.
Lack of In-House Experts
Integrating techs like machine learning into intricate healthcare workflows requires excellent technical skills and a deep understanding of medical science. Many AI startups need to incorporate clinical expertise during early development phases. Meanwhile, many experienced clinicians need a sufficient understanding of machine learning to contribute effectively.
After all, many organizations intend to avoid undergoing major changes in ML tech adoption. That is because they should redefine team roles, invest in change management, and initiate workforce reskilling programs. These moves will prove worthwhile in the long term.
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Final Words
Machine learning transforms healthcare by enabling innovative disease discovery, personalized treatments, and more efficient clinical processes. ML’s applications in the medical sector are vast and impactful, ranging from drug discovery to medical imaging to personalized medicine. As technology evolves, its role will expand, leading to better patient outcomes and more efficient care delivery. After all, by leveraging advanced algorithms and extensive datasets, ML reshapes medical diagnostics, patient management, and medical research, driving the industry’s future.
SPsoft specializes in building custom ML solutions that optimize the diagnostics process, enhance patient monitoring, and streamline operations. Get in touch with us to explore our expertise!
FAQ
Machine learning in healthcare includes multiple applications such as predicting disease outbreaks, modifying patient behavior, virtual nursing, and analyzing medical images. It also aids in accurate diagnostics, robot-assisted surgeries, drug discovery, and optimizing hospital management. ML enhances personalized treatment plans, improves patient monitoring, and helps detect high-risk patients, leading to better patient outcomes and efficient care delivery.
