It is one of the most exciting areas being developed in computer assisted health care.
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Written by Coursera • Updated on Sep 14, 2022 Show
Learn more about machine learning in healthcare. Find out how artificial intelligence can improve health care and what exciting careers are available in this field. Machine learning is a tool used in health care to help medical professionals care for patients and manage clinical data. It is an application of artificial intelligence, which involves programming computers to mimic how people think and learn. In health care, you can apply this to collect and manage patient data, identify health care trends, recommend treatments, and more. Hospital and health care companies have begun to recognize the ability of machine learning to improve decision-making and reduce risk in the medical field, which has led to several new and exciting career opportunities. Machine learning in health care is an evolving field that is more accessible than people may realize. While the terms “artificial intelligence” and “machine learning” might initially seem intimidating, many machine learning principles rely on fundamental mathematical and programming skills. Once you understand the basics behind machine learning, you can build these skills to address more advanced concepts and challenges. This can uncover new opportunities for innovation and diverse career paths in the health care space. Read more: Is Machine Learning Hard? A Guide to Getting Started Rise of ML in health care settingsAs technology expands, machine learning provides an exciting opportunity in health care to improve the accuracy of diagnoses, personalize health care, and find novel solutions to decades-old problems. You can use machine learning to program computers to make connections and predictions, and discover critical insights from large amounts of data that health care providers may otherwise miss — all of this can add up to a direct impact on the health of your community. The goal of machine learning is to improve patient outcomes and produce medical insights that were previously unavailable. It provides a way to validate doctors’ reasoning and decisions through predictive algorithms. For example, suppose a doctor prescribes a specific medication for a patient. In that case, machine learning can validate this treatment plan by finding a patient with a similar medical history who benefitted from the same treatment. Machine learning and the Internet Of Medical Things in health careWhen you use machine learning in health care, you rely on an ever-evolving patient data set. You can use this data to find patterns that allow medical professionals to recognize new diseases, make decisions about risks, and predict treatment outcomes. Because of the volume of patients and the diverse medical technologies used to collect data, having medical devices sync to a central “network” is a convenient way to compile large volumes of information. The Internet of Medical Things (IoMT) is the network of medical devices and applications that can communicate with one another through online networks. Many medical devices are now equipped with Wi-Fi, allowing them to communicate with devices on the same network or other machines through cloud platforms. This allows for things like remote patient monitoring, tracking medical histories, tracking information from wearable devices, and more. As more wearable and internet-equipped medical devices come onto the market, the IoMT is predicted to expand exponentially. Types of AI relevant to health careMachine learning falls under the broad category of artificial intelligence. While many types of artificial intelligence exist, certain ones are more applicable to the needs of the health care industry. Machine learning engineers in health care often focus on streamlining medical administrative systems (such as health care records), finding trends in large clinical data sets, and creating medical devices to assist physicians. Within these focus areas, some of the most common types of artificial intelligence used are: Machine learning – neural networks and deep learningNeural networks often referred to as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning that imitates the structure of the neural networks in our brain. You can use ANNs in the health care field to produce computer-generated outcomes similar to what human reasoning would lead to when making a diagnosis. ANNs are the basis of deep learning, which is the ability of the ANN to learn from large amounts of data. In the health care field, you can use deep learning to analyze MRI and other medical images to detect abnormalities. This doesn't replace the doctor's role, but it enhances the doctor's work by speeding up the time it takes to form a diagnosis and start patient treatment sooner. Natural language processingNatural Language Processing is a machine learning type centered around the computer’s ability to understand, analyze, and generate human language. You use natural language processing to interface and communicate with the machine. One application of natural language processing in health care is pulling patient data from doctors' notes. Physical robotsPhysical robots are what they sound like — robots that are physically present in the room with a doctor. Robots can support surgeons during complex procedures that require precise movements. In many cases, robotic surgery reduces the procedure's invasiveness, which can also lower complications and improve outcomes. Robotic process automationRobotic process automation is a type of machine learning that mimics human actions for manual tasks such as data entry. Medical companies and hospitals use machine learning to automate these tasks. This can free up the time of physicians and medical administrators to devote their efforts to more valuable activities. Read more: How to Get a Job in Artificial Intelligence Applications of machine learning in health careWhile new machine learning applications emerge all the time, the most common applications in health care are centered around improving the quality of care and patient health outcomes. Because of the broad uses for machine learning in health care, you may choose an area of specialization. Understanding the different applications of machine learning in health care (like the ones listed below) can help you find the concentration that best suits your personal interests and career goals.
Ethics of machine learning in health careWhile machine learning is an exciting frontier in health care, it comes with several ethical considerations. For one, the transfer of medical decision-making from solely human-based to the use of smart machines raises questions about privacy, transparency, and reliability. Patients cannot discuss their care with machines as they can with a physician, which can provide stress and uncertainty during the diagnostic process. Patients may also rather hear negative health care news from a physician they trust than a machine. In addition to this, mistakes in patient diagnosis are likely unavoidable, and medical facilities may try to avoid accountability for who is responsible for an inaccurate AI-assisted diagnosis. Machine learning engineers also have the potential to create biased algorithms accidentally, and predictions may be more or less accurate based on gender or race. As the field of machine learning continues to further integrate into health care, governing bodies and clinicians must establish clear boundaries, protocols, and accountability early on to minimize later consequences. How to learn machine learning for health careTo learn machine learning for health care, you can study how machine learning works and develop your computer systems and coding skills. A background in mathematics or computer science — or at least an affinity for the topics — can be helpful. Building your knowledge of medical procedures and terminology can also be beneficial. Degree optionsAlthough finding a job working with machine learning in health care is possible, you may enjoy boosting your knowledge by earning a degree. A degree also can help you stand out from the competition when you apply for a job. Consider a bachelor's or master's degree in one of the following majors:
Read more: What Is an Undergraduate Degree? SkillsMost people who work in machine learning have strong computer programming skills. Some of the field's more commonly used coding languages include C, C++, Java, Julia, Python, R, Java, and Scala. In addition to coding in these languages, ML workers often understand the theory behind the algorithms used in programming and modeling. This includes algorithms across supervised learning approaches, unsupervised learning approaches, reinforcement learning approaches, and deep learning. Depending on the exact nature of the job, the emphasis and requirements will vary. Often, you will use a mix of computer program foundations, software engineering and design, data science, and machine learning skills. Employers may also recommend you have proficiency with popular machine learning software, such as IBM Watson, Amazon, Google Cloud, and Microsoft Azure. CertificationsWhile there are no formal certification requirements to be a machine learning professional, having a Certificate in the area may strengthen your application. Specializations and Professional Certificates like Mathematics for Machine Learning from Imperial College of London or IBM Machine Learning Professional Certificate can help you build your knowledge and skills in the area. professional certificate IBM Machine LearningMachine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis. 4.6 (807 ratings) 3,176 already enrolled INTERMEDIATE level Average time: 8 month(s) Learn at your own pace Skills you'll build: Artificial Intelligence (AI), Machine Learning, Feature Engineering, Statistical Hypothesis Testing, Exploratory Data Analysis, Regression Analysis, Supervised Learning, Linear Regression, Ridge Regression, Machine Learning (ML) Algorithms, Decision Tree, Ensemble Learning, Classification Algorithms, Dimensionality Reduction, Unsupervised Learning, Cluster Analysis, K Means Clustering, Principal Component Analysis (PCA), Deep Learning, Artificial Neural Network, Reinforcement Learning, keras, Python Programming, Data Analysis, unsupervised machine learning Machine learning in health care career prospects, jobs & salaryThe demand for ML professionals in health care will likely rise over the next decades as doctors and health care facilities incorporate it into their practices. As you consider your career prospects, you may find it helpful to look at the various jobs available in the field along with their salaries.
Ready to take the next step in your career?Explore the exciting world of machine learning engineering in health care through courses offered by the world’s top universities on Coursera. Online courses like Fundamentals of Machine Learning for Healthcare or AI in Healthcare, offered by Stanford University, can help you determine if this is your career path. What’s more, the broad course offerings on Coursera allow you to find your niche and tailor your skill set to the career path that best fits you. Build your resume, your skill set, and your passion. course Fundamentals of Machine Learning for HealthcareMachine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize ... 4.8 (241 ratings) 11,632 already enrolled BEGINNER level Average time: 1 month(s) Learn at your own pace Related articles
Article sources 1. Glassdoor. "Artificial Intelligence Engineer Salaries, https://www.glassdoor.com/Salaries/artificial-intelligence-engineer-salary-SRCH_KO0,32.htm." Accessed June 28, 2022. 2. Glassdoor. "Data Scientist Salaries, https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm." Accessed June 28, 2022. 3. Glassdoor. "Healthcare Technology Consultant Salaries, https://www.glassdoor.com/Salaries/healthcare-technology-consultant-salary-SRCH_KO0,32.htm." Accessed June 28, 2022. 4. Glassdoor. "Machine Learning Engineer Salaries, https://www.glassdoor.com/Salaries/machine-learning-engineer-salary-SRCH_KO0,25.htm." Accessed June 28, 2022. 5. Glassdoor. "Applied Machine Learning Scientist, https://www.glassdoor.com/Salaries/applied-machine-learning-scientist-salary-SRCH_KO0,34.htm." Accessed June 28, 2022. 6. Glassdoor. "Pharmaceutical Data Analyst Salaries, https://www.glassdoor.com/Salaries/pharmaceutical-data-analyst-salary-SRCH_KO0,27.htm." Accessed June 28, 2022. Written by Coursera • Updated on Sep 14, 2022 This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. What are the uses of computer assisted learning?They allow students to progress at their own pace and work individually or problem solve in a group. Computers provide immediate feedback, letting students know whether their answer is correct. If the answer is not correct, the program shows students how to correctly answer the question.
How computer assisted learning is useful for weak students?Computer Assisted Learning (CAL) provides students with two important opportunities: remedial academic support and exposure to computers. Studies have shown that access to computers with educational software can significantly improve student performance in disadvantaged schools.
What is the benefit of patient education using computer assisted instruction?For the children who participated in these studies, computer-based patient education was effective in changing their health care behavior and health outcomes, including improved knowledge and communication with parents and care providers and a reduction in urgent medical care.
What do you mean by computer aided teaching testing?Computer aided assessment facilitates a detailed analysis of test results with minimal effort. This can be used to identify areas within the course where the students have difficulty, thus alerting staff to the possible need to adapt their teaching.
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