Top Machine Learning Applications in Healthcare. Part 1

The adoption of machine learning in healthcare is rapidly increasing. According to Grand View Research, the global AI market in the medical domain was valued at $22.45 billion in 2023 and is anticipated to grow at a compound annual growth rate (CAGR) of 36.4% from 2024 to 2030.

Machine learning applications are diverse, including patient apps, e-triage tools, online symptom checkers, virtual agents, and bionic pancreases for diabetic patients. Here are notable use cases and real-world examples showcasing the potential of ML in medical practices.

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Predicting Disease Outbreaks

Satellites can process vast amounts of real-time and historical data, which predictive analytics tools can analyze to forecast potential disease outbreaks. For example, malaria outbreaks can be predicted by examining monthly rainfall, temperature, and other parameters. That primarily benefits developing countries that lack the medical infrastructure and education to combat such diseases. Early detection allows governments to implement preventive measures.

Example: ProMED offers an online, real-time analysis and reporting system for infectious disease outbreaks worldwide and exposure to toxins affecting human or animal health. It collects data from various sources, including official reports, media, and subscribers. An expert team reviews these reports before they are accepted into the system. The data provided by ProMED is visualized on HealthMap, illustrating disease outbreaks in each country.

Modifying Patient Behaviors

Many common diseases are manageable or avoidable. For instance, a healthier lifestyle can help prevent or detect early type 2 diabetes, obesity, and heart disease. However, lifestyle changes require consistent reminders and follow-ups. Machine learning algorithms can aggregate health data from patients' connected medical devices and sensors, generating insights into their behavior and guiding them on their transformational journey.

Example: SmokeBeat is a smoking cessation app that collects data on the user's smoking behavior using an accelerometer on a smartwatch or smart band to detect hand-to-mouth gestures. This data is then processed by a machine learning algorithm, which provides real-time cognitive behavior therapy incentives. User responses to these incentives are measured and recorded to improve effectiveness. The app fosters a supportive social network by comparing users' smoking data with their chosen peers, creating a sense of encouragement and solidarity.

Promoting Virtual Nursing

Healthcare facilities are increasingly utilizing virtual nurses to manage various healthcare tasks. These computer-generated avatars, designed to be social, empathetic, and informative, are available 24/7. This approach ensures patients can get their questions answered and concerns addressed at any time, even between doctor visits, providing thorough and effective service.

Example: Molly, a virtual nurse, demonstrates the convenience of this innovative approach. This female avatar remotely monitors medical conditions by receiving data like blood pressure and weight from patients' monitoring devices connected via Bluetooth. Such devices are placed in patients' homes, making it easy to take measurements as often as needed. Molly recognizes speech and verbally answers patients' queries and offers a chatbot for private discussions.

Analyzing Medical Images

Despite advancements in healthcare technology and data science, medical image analysis remains a meticulous and error-prone task, underscoring the need for attention to detail. Machine learning 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, developed by Subtle Medical, is ML-based software that enhances MRI image quality. Denoising and resolution enhancement techniques improve the sharpness of images from MRI scanners. RadNet, a leading outpatient imaging provider in the USA, reported a 33-45% acceleration in their protocols after adopting SubtleMR technology.

Identifying High-Risk Patients

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 enable clinicians to identify high-risk patients quickly.

Example: Face2Gene is a precision medicine app that employs ML-enabled facial recognition technology to help clinicians diagnose rare diseases more accurately. With machine learning, the app can detect phenotypes, identify relevant facial features, and assess the likelihood of a patient having a particular syndrome.

Are you curious about leveraging ML techs to increase patient engagement and care quality? Write to me at [email protected], and we will gladly assist you with your healthcare project.

Ultimately, machine learning use cases in healthcare are vast and impactful. They transform the medical industry by ensuring innovative disease discovery, personalized treatment plans, and more effective clinical operations.?