Will MLOps Change the Future of the Healthcare System?

In the era of modern technology, innovations are going on day by day. Machine learning and Artificial intelligence make it easy to solve daily problems by automating tasks and reducing the human cost. Machine learning operations are used to automate the deployment, monitor, and manage machine learning models in production. In healthcare systems, many clinical and laboratory activities are done with the help of analyzing a lot of data. Machine learning algorithms and deep learning algorithms analyze this and extract insights from this data that solve the problems related to healthcare. MLOps finds the solution in many sectors such as finance, healthcare in the retail industry.

In this article, we will see How MLOps change the future of the healthcare system, the Importance of MLOps in Healthcare, How Machine learning Reinventing MLOps, its Uses, and the future of MLOps.

MLOps

Introduction

As we know health is wealth, healthcare is very import part of the human activity. Healthcare is the another sector that is affected by the technology. The activities of clinical and laboratory are solve with the help of lots of big data. By analyzing this big data the problem of healthcare is solved. Data science is an increasing field of science and is increasing day by day. There are many field in healthcare where the technology is find its way such as medical imaging, predictive diagnosis and many others. On a surveys, it is found that 90% of the machine learning algorithms are not deployed and 10% of the machine learning algorithm are worked. Data scientist are working day by day to improve the MLOps in the healthcare that is can be used for analyzing the large data and can be use for predictive analysis.

Importance of MLOps in Healthcare

MLOps plays an important role in Healthcare industry that provides enhanced model and manages model in production by conforming that models are implemented efficiently and update that give a quick result in healthcare workflows. The monitoring and continuous optimization of the machine algorithms by MLOps helps in maintaining the performance of machine learning algorithms time to time. This is important in healthcare industry that provides an accurate predictions and diagnoses for patient outcomes. The data of healthcare is sensitive and MLOps helps us to provide data security and privacy about the data. MLOps provides collaboration between data scientists, IT professionals, and healthcare domain experts. This collaboration of MLOps provides a way for developing effective machine learning algorithm that solve healthcare problems. MLOps enhances the development of machine learning algorithms that reduces the time take to move from research and development to deployment. It is very important to make the algorithm fast in healthcare that can impact patient outcomes.

How is Machine Learning Reinventing Healthcare?

Machine learning is playing a great role in reinventing healthcare in various domains specially in healthcare that are illustrated with the following points:

1. Prediction and diagnosing disease: Medical images such as X-rays, MRIs and CT scans are analyzed with the help or machine learning algorithm that helps in early detection and diagnosis of diseases such as cancer. The patient data is analyzed with the help of ML models to predict the diseases that allows for early treatment plans.
2. Development and discovery of drug: The biological and drug data is analyzed to find the potential drug targets with the help of machine learning models to enhance the process of discovering drug. The existing drugs are analyzed with the help of machine learning algorithms to find new therapeutic uses, that reduces the time and cost of development of drug.
3. Genomic Medicine: The genomic data that is large-scale is analyzed with the help of machine learning that provides information of genetic variations and their associations related to diseases. This information is very helpful for personalizing medicine and therapies that are targeted.
4. Understanding Health Records with the help MLOps: MLOps helps in finding the valuable information from the medical data that is unstructured such ash health records for improving the usability of healthcare data.

Use of MLOps in Healthcare

MLOps plays an important role in healthcare industry in successfully deploying the machine learning algorithm, managing and optimizing the machine learning models in healthcare industry. There are many uses of MLOps in Healthcare.

1. Integrating and Deploying machine learning algorithm: MLOps helps in deploying the machine learning algorithms on production conforming a integrating with healthcare systems that is already built. These machine learning algorithm in the production used for prediction and supporting tools that is accessible to healthcare professional.
2. Regular Monitoring of the model: MLOps provides the facility of monitoring the machine learning algorithms in real-time. Healthcare uses tools for the monitoring the performance of the model to detect anomalies and confirms the model is accurate and can be operate in production easily.
3. Automatic Testing and Validation of the machine learning model: In healthcare it is necessary to test and validate the machine time to time to confirm the reliability of the machine learning models. MLOps in healthcare test and validate the machine learning models automatically for enhancing the performance of the machine learning models.
4. Prevention and Prediction of Disease: MLOPs can be helpful in predicting the disease and preventing the disease with the help of suggestion. The machine learning algorithm is trained one image data to detect the disease and predict the disease. This helps healthcare industries to intervene early by offering preventive care and reduce the burden of healthcare
5. Medical imaging and diagnosis: The machine learning algorithm are trained to analyze the medical images such as tumors detection in radiology images, anomaly identification in X-rays or diagnosis of skin conditions with the help of dermatology images.
6. Personalized medicine: MLOPs in healthcare provides the facility of personal treatment that is based on patient health condition or characteristics, genetic information and health data of the patient. This helps in more effective therapies that minimizes the adverse reactions and improve the patient health.
7. Developing and discovering of drug: MLOps in healthcare industry can be used to increase the process of drug discovery by analyzing the big data to find the drug for the disease and predict the drug. This can be helpful in reducing the complex workflow of drug development.

Real-Life Examples of MLOps in Healthcare: Ortho Baltic Case Study

Ortho Baltic is a device related to orthopedics that is manufactured by Europe and have used in development of patient-specific implants. Ortho Baltic is data-driven model built by collaborating with EasyFlow that is used for implant production that reduces human error and processing time. It’s very difficult and time-consuming and erroneous for engineers to reconstruct the 3D anatomical models from CT scans. CT scanned images consists of noise that is obtained from metal artifacts that can be complicated to identification of anatomical landmark. Ortho Baltic collaborated with EasyFlow for automating 3D model that is used for reconstructing and data cleansing of the CT scanned images. This device is significantly better than human that reduces the task to completing it. This real-life example of MLOps in healthcare show the revolution of MLOps in healthcare by automating the process and improve the decision in personalized medicine.

Frameworks of MLOps in Healthcare:

MLOps in healthcare involves monitoring, deploying and managing machine learning algorithms in production. Here are some frameworks used in MLOps in Healthcare that are discussed as:

1. TensorFlow Extended (TFX): TensorFlow Extended is a platform that is designed for deploying the machine learning algorithm on the production integrating with TensorFlow that make it easy for healthcare machine that is based on TensorFlow. TFX can be used for medical imaging, patient diagnostics, and other healthcare applications.
2. MLFlow: Open-source platform that is used to manage machine learning algorithm on MLOps. It involves packaging code and share and deploy models in production. MLFlow used in healthcare for tracking experiments while developing machine learning algorithms and manage and deploy these machine learning algorithms in production.
3. Kubeflow: Open-source platform used for managing, deploying machine learning algorithm and Kubeflow is built on Kubernetes. Kubeflow in healthcare used for monitoring real-time patient or predictive analytics.
4. Version Control of health data: Data Version control is an open-source platform where the version of the machine learning algorithms is controlled. DVC helps in managing the version of the data sets. It is helpful in healthcare scenarios where data is important for training and validating machine learning algorithms conforming the changes in data.

What is the Healthcare DataScientist Do?

The main role of a healthcare datascientist involves applying datascience techniques and methodologies to healthcare data to extract valuable insights , improve decision-making, and enhance patient outcomes. This role plays a important part in leveraging the power of data to enhance patient care, optimize patentient health through data-driven insights and solutions.

Future of MLOps in Healthcare systems

As the technology is increasing day by day in any filed . The future of MLOps in healthcare is increasing day by day. The technology is regularly advancing and healthcare industries are taking the help of machine learning algorithms to solve the problem, MLOps in healthcare deploys and manages machine learning algorithms in production. MLOps integrates with Electronic Health Records (EHRs) to make the task easy enabling healthcare industries to deploy machine learning models in the production directly that improves the accessibility and can make decisions. As the use of MLOps in healthcare is increasing, the focus of making the machine learning algorithm is more that makes the machine learning models more accurate, interpretable and explainable. MLOps provides automatic mechanisms that involves healthcare regulations, bias monitoring and security concern.

Conclusion

MLOps in healthcare can be used to enhance integration, security, accuracy and the adaptability of the increasing technologies. As the machine learning technologies are increasing day by day in healthcare industry that plays a great role in responsibility and effective deployment of machine learning learning algorithm in production improve patient outcomes and improve healthcare operations.