Top Machine Learning Applications in Healthcare. Part 2

A report from Meticulous Research forecasts that the global ML market in healthcare will grow to $19.25 billion by 2027, achieving a CAGR of 44.9% from 2020 to 2027. Machine learning transforms how diseases are discovered and treated, motivating healthcare professionals to explore new possibilities. Today, we’ll continue to analyze the tech’s most common use cases. 

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Performing Surgeries with Robots

ML-powered robotic surgery assistants are revolutionizing healthcare. These robots enhance precision, access different body areas with minimal intervention, and reduce operation time. This advancement eases the burden on human surgeons, making it a promising development for the future of care delivery. They are invaluable for performing complex surgeries, reducing patient trauma and recovery time, and are particularly useful in microsurgery.

Example: Senhance, a console-based, multi-armed surgical system, is remotely controlled by surgeons. It leverages ML and deep learning models to perform complex procedures. Surgeons can undergo simulation training during the preoperative stage using an ML-driven database. During surgeries, the system's Intelligent Surgical Unit adjusts the camera view. It predicts when a surgeon needs to zoom in or enhance real-time images based on eye-tracking camera data.

Improving Drug Discovery

The drug discovery process is expensive and time-consuming, as researchers must test thousands of elements and their combinations to find a viable drug. Machine learning algorithms streamline this process by:

• 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, developed by Google's DeepMind, is a machine learning-powered system that automatically predicts protein structures. This biological breakthrough accelerates drug screening and development by predicting protein interactions. In 2021, Google launched Isomorphic Labs to use AlphaFold's technology to find cures for prevalent diseases.

Optimizing Hospital Management

Hospital management systems are becoming more complex with the increasing demand for healthcare services. Implementing ML-based systems streamlines administrative processes, enhancing executive functions. They can improve staff scheduling, optimize supply chain and inventory management, facilitate resource allocation, and streamline EMRs management.

Example: Globus.ai has developed a system that helps medical institutions streamline staffing. Using natural language processing and ML, the system matches healthcare employees to specific tasks based on their skill sets, making task scheduling more efficient. It also considers legal requirements, such as limits on working hours or the need for specific expertise.

Health Insurance

Health insurance is essential for making healthcare accessible, yet many processes remain manual and inefficient. Machine learning can enhance 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: 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%.

Are you curious about leveraging ML techs to increase patient engagement and care quality? Write to me at mlazor@spsoft.com, and we will gladly assist you with your healthcare project.

As machine learning advances, its influence will grow, resulting in improved patient outcomes and more efficient care delivery. Utilizing sophisticated algorithms and vast datasets, the tech is transforming medical diagnostics, patient management, and research, paving the way for the future of the entire industry.