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Machine learning in healthcare got popular

Machine learning in healthcare has emerged in recent years. The use of machine learning in healthcare helps to analyze complex medical data in the treatment process.

From its undoubted reception as a buzzword to multiple applications in healthcare, machine learning has emerged. We go through core concepts, challenges, and uses of machine learning in various aspects, especially healthcare. Machine learning in healthcare is a study of computer science for automation through past experiences. Machine learning is a subset of AI developing computer systems to perform tasks that require humans.

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Machine learning in healthcare plays an important role in healthcare such as bioinformatics, gene detection for cancer, etc. This article provides the basis of machine learning in healthcare such as definition, benefits, applications, and trends. Healthcare software development also promotes the adoption of machine learning in healthcare. So what defines what makes machine learning in healthcare popular in recent years?

Machine learning in healthcare: A brief introduction

What is machine learning in healthcare?

Before going to the definition of machine learning, we take a look at the Artificial Intelligence concept. AI refers to any technique that makes computers imitate human behaviors to solve problems. A subset of AI, machine learning uses statistics to recognize patterns by learning and improving through experiences on previous data. Computers gather data and statistics and then learn from that data by developing algorithms.

Machine learning in healthcare helps patients and clinicians in many approaches. The most common machine learning applications in healthcare are automating medical billing, clinical decision, and clinical care guidelines. Researchers have developed the first machine-learning algorithm to predict toxicities in patients in their radiation cancer therapy for head and neck.

Besides, machine learning in healthcare of Google helps to detect breast cancer with 89% of accuracy. Machine learning in healthcare has also promoted the introduction of medical software development in treatment.

Types of machine learning in the medical field

Machine learning develops foundational models to address problems through data and statistics. This method has boosted the learning process and provided accurate results. In particular, types of machine learning in the medical field are supervised, unsupervised, semi-supervised, and reinforcement learning.

  • Supervised learning: Supervised learning involves training the model based on labeled data. Then supervised learning allows this model to make predictions for a new set of data. It will classify data into training and testing.
  • Unsupervised learning: It involves training data with an unlabeled value or target value. Machine learning will cluster the type of data by defining the hidden data patterns. This unsupervised learning tries to discover the data patterns.
  • Semi-supervised learning: a blend of supervised learning and unsupervised learning. It takes both labeled and unlabeled data. This semi-supervised learning detects outliers.
  • Reinforcement learning: This type of machine learning develops a system which improves business performance by collecting feedback. It is an interactive process that means learning from the environment with any help from humans.

As the healthcare market has emerged, there is health information. So doctors can not process without any help from machines. So machine learning in healthcare gives techniques to recognize data patterns and uses algorithms to predict possible outcomes.

Besides, different types of healthcare data have come in the healthcare sector such as clinical, sensor, omics data, etc. Healthcare software development also helps to identify patterns of data and predicts results of people’s health. Many distinctive methods extract relevant information and then different algorithms give a better prediction. Here are some healthcare data that people put in machine learning in healthcare for analyzing:

  • Clinical data: people collect clinical data during patient treatment such as laboratory tests, radiology images, allergies, etc. For example, a report by Weng et al. cited by used machine learning in healthcare algorithms such as random forest, logistic regression, neural networks.
  • Sensor data: is an ordered sequence of pairs such as time-series signals. This data can be simple numerical, categorical data, or even more complex processed by computing devices. Moreover, it can be wearable sensor data collected from arms, hands, and thighs, or even wireless sensors using mobile apps.
  • Omics data: is a collection of complex and high dimensional data such as genomics, transcriptomic, and proteomics. Handling this data requires various techniques in machine learning in healthcare algorithms. So many healthcare software development services join hands to develop medical software to solve complicated cases.

Machine learning and healthcare: Benefits & How to use

Use of machine learning in healthcare

The fundamental goal of machine learning in healthcare is to support doctors’ works by providing better service and care. Take a look at some examples of the use of machine learning in healthcare.

Use of machine learning in healthcare in personal treatment
One of the cases of machine learning in healthcare is customized treatment. People use personal data and analysis techniques to improve personalized healthcare services. Machine learning algorithms develop personalized treatment systems based on patients’ statistics and symptoms. By combining clinical, socioeconomic, and pharmaceutical data with ML algorithms, doctors observe the effectiveness of personal treatment. Additionally, healthcare software development services can also develop custom apps to manage medical treatment for individuals.

Machine learning and healthcare service management
As stated in pharmaceutical research, machine learning in healthcare thrives in many areas of operations and management. Nevertheless, the treatment journey within hospital zones creates administrative tasks and departments for patients. So these processes will impact doctors, nurses, and patients. It is a complicated journey and leads to high costs for hospital operators.

Medical software development services introduce healthcare apps that help to automate administrative tasks to simplify treatment journeys. The adoption of machine learning in healthcare journeys improves care, supports medical staff in their medical studies, and optimizes costs.

Providing virtual assistants for patients
Virtual assistants are a healthcare technology solution through chatbots or even dedicated applications. In particular, machine learning in healthcare has recently emerged to support patients during their treatment journeys. For example, virtual nurses are available to answer questions or requests immediately and provide advice or directions. This virtual assistant can record interactions and disease information so doctors use them in the treatment process for patients.

Furthermore, virtual assistants can generate significant savings for hospitals by reducing stresses related to medical care. The adoption of healthcare software development can remind patients if they have medical checks for each month.

Innovative developments of new drugs
The use of machine learning in healthcare at the early stage of drug discovery has potential for various services. Machine learning in healthcare can help to develop new drugs from initial screening to the expected success rate. It also helps to predict the effectiveness and safety of new drugs. Machine learning in healthcare is beneficial for drug development since each compound has a specific purpose. It will limit the drug development to a small number of compounds.

The use of machine learning in healthcare is essential for doctors to provide diagnoses for patients during their treatment. Machine learning in healthcare can help to save time and optimize correct diagnosis through healthcare software development services. Furthermore, it opens more perspectives in the prediction of possible diseases.

  • Heart disease diagnosis: people are using machine learning in healthcare to diagnose heart disease. Heart disease diagnosis is a topic of much research globally. Thus, the automated heart disease diagnostic program is one of the human achievements in the 21st century.
  • Liver disease: The use of machine learning in healthcare helps to predict liver disease. Predicting liver disease is a challenging task since many possible diseases can affect the liver. Besides, it requires a large amount of data on liver disease.
  • Cancer detection: Machine learning in healthcare can detect different types of cancer thanks to the adoption of AI. Scientists have upgraded the algorithms which observe and detect different forms of cancer.
  • Diabetes: Diabetes damages many body parts such as the heart, kidneys, and nervous system. ML in healthcare will identify markets of diabetes at the early stage to save patients’ lives. Healthcare staff can use algorithms of machine learning in healthcare to predict diabetes.

Benefits of machine learning in healthcare

Medical imaging - One of the largest benefits of machine learning in healthcare
Computer visions are one of the most significant healthcare sectors owing to the technology in healthcare. Many firms in the medical industry are combining cognitive computing and genomic tumor sequencing to introduce healthcare software development. It will help to give precision medicines. Executing machine learning in healthcare will define diabetic retinopathy and macular edema in the photographs. Thus, improving medical imaging is one of the best benefits of machine learning in healthcare that you might know.

  • Robotic surgery: Recently, robotic surgery has gained massive popularity. Machine learning in healthcare technology uses robots for surgical processes in healthcare. Replacing human surgeons with robotics brings tremendous benefits such as operations in small spaces, radically increasing the chances of human-based problems such as shaking hands. Machine learning in healthcare, particularly robotic surgery focuses on machine vision. Besides, it can evaluate distances to a higher degree of accuracy and identify specific body parts or even organs within a body.
  • Generating electronic healthcare records: There is a series of patient data during their treatment processes. Hospital operators in the healthcare industry should record healthcare data from patients. Thus, machine learning applications in healthcare will develop electronic smart records for the treatment processes. It will keep medical records, health conditions and even suggest treatment plans if required through healthcare software development services.

Machine learning applications in healthcare

Chatbot - An example of machine learning in healthcare

A chatbot is a computer system that allows humans to use natural human languages to interact with people. Some examples of chatbots are AliMe, DeepProbe, RubyStar, etc. Healthcare software development also supports hospitals to introduce chatbots through mobile apps. These chatbots can answer questions or requests from patients 24/7. Initially, people define targeted topics or conversations and then develop model conversations based on these topics. A chatbot recognizes the user input via many forms and accesses the information to provide knowledge.

Patient care during and post their treatment journeys

Chatbots have the potential to improve medical communication between clinic-patient and doctor-patient. It helps meet the high demand for healthcare services such as remote testing, monitoring of medical check-ups, and telephone consultations. The use of machine learning in healthcare can conduct health surveys, set up personal medical reminders, book appointments, etc.

Additionally, chatbots provide fast responses to questions while seeking specific symptoms in possible diseases through mobile apps. Healthcare software development services can collect data in several ways such as text-based services, mobile applications.
A study shows the effectiveness of healthcare software (iDecide bot) on predicting cancer. This healthcare software provides cancer knowledge, which helps to reduce anxiety and depression after using iDecide bot.

Machine learning applications in healthcare: Radiology and Radiotherapy

People use machine learning in healthcare to automatically classify patient data. These data can be behaviors, demographics, decisions, emotions, clinical factors, etc. A recent study shows that 80% of people are looking for medical-related information via online channels. Online support groups give them a comfortable space to get advice, express emotions, and share information through mobile apps.

Radiology educational posts on several social media sites are also beneficial. For instance, OnBot uses suitable mapping techniques to transform information and knowledge into a database and then utilize the data to run a chatbot.

Machine learning in healthcare can take advantage of the big data cloud. A mobile healthcare app calculates the monitor unit and even provides the treatment dose or daily dose. Telehealth is a kind of service administered through telecommunications or technology in healthcare. It allows remote bidirectional communication such as visual and audio interaction with users. For example, patients with cancer follow telephone communications in prostate, brain, colorectal, and bladder cancer.

Education and knowledge transferring systems

To provide practical healthcare workforces, interprofessional education through mobile apps is essential. It allows more than one professor to learn from others or each other, which increases collaboration and promotes healthcare service quality.
Moreover, people can use high-qualified healthcare information from the online discussion for conducting a chatbot. The chatbots present high potential in far-distance education and even act as undergraduate advisors. The use of machine learning in healthcare can reduce costs dramatically and make treatment sessions more engaging to experienced doctors.

Quick responses, especially during a covid-19 outbreak

In emergencies, a few minutes can save one’s life. So quicker healthcare and accessibility can save more lives. Machine learning in healthcare can distribute medical supplies on-site in many emergency cases. People can apply this system in urban, semi-urban, and remote areas. ML in healthcare allows hospitals to provide specialized treatments despite the absence of medical staff.

The covid-19 outbreak affects the cardiovascular system of patients. So it requires fast and specific tools to identify the symptoms early. Many countries are over-stretching their healthcare sources to prevent the pandemic spread. Artificial Intelligence helps to monitor and control the pandemic in some areas. It also helps to detect the disease at the early stage through healthcare software. Moreover, AI plays an important role in analyzing data to aid covid-19 research.

Final thoughts

Machine learning in healthcare is a robust and powerful tool that can benefit the healthcare industry. The use of machine learning in healthcare helps physicians and scientists to develop personalized therapies and improve the quality of people’s life. So what makes machine learning in healthcare so popular? Machine learning is promising to develop advancements in the healthcare industry so extensive that we cannot imagine.

Top comments (1)

geremy735 profile image

Experience the paradigm shift in healthcare through Artificial Intelligence, as it revolutionizes processes and outcomes. One remarkable application is in radiology, where AI is transforming the analysis of radiology images. From detecting subtle anomalies to expediting diagnoses, AI's precision enhances accuracy and expedites decision-making. It's not just about advanced technology, but also about improving patient care by minimizing errors and reducing wait times. Embrace the future of healthcare with AI, where innovation meets compassion, and witness how radiology images are unlocking new realms of efficiency and patient well-being.