In our previous blog, we discussed how artificial intelligence AI in healthcare is transforming medical diagnostic labs. In this blog, let’s explore an essential part of AI in healthcare: machine learning (ML). Healthcare machine learning algorithms analyze vast medical data. ML diagnosis technology helps identify patterns that signify a particular disease or condition. As a result, it largely contributes to making quick decisions and early diagnoses.
Automating tasks that consume a considerable amount of time and resources can significantly optimize a lab’s performance. Additionally, to understand the relevancy of ML in a diagnostic lab, let’s deep dive into the future of medical diagnostic technologies. We can explore how the advantages of machine learning, as part of AI, can be a game changer in healthcare.
Is Machine Learning the Future of Medical Diagnosis?
Machine learning techniques have penetrated every aspect of our daily lives. From internet searches to voice recognition services, they are still expanding into new sectors. Healthcare is one such industry that focuses on implementing the latest technologies, and machine learning (ML) is one of them.
Applications of Machine learning in healthcare are a subset of AI technology. AI and ML in healthcare use statistical models and algorithms. These enable computer systems to learn and improve their performance from experience on a particular task. Implementing ML in medical diagnosis allows medical professionals to make faster and more accurate diagnoses, ultimately leading to better patient outcomes.
Medical diagnostics can use ML for various tasks, including medical image analysis, health care analysis using machine learning, disease detection, risk assessment, and personalized treatment recommendations. Additionally, AI and machine learning in healthcare can analyze X-rays and MRI scans, examine patient information, including test results and medical history, and offer customized treatment plans.
Considering all the above factors, it is yet too early to decide the future of machine learning in healthcare. Looking at how quickly the medical field adopts technologies; it would be apt to say that role of machine learning in healthcare can significantly alter the medical diagnostics landscape. Further, let’s look at some existing machine learning medical applications and analyze their prospective advantages and challenges. After reading this article, you will better know the importance of machine learning in healthcare and how it can transform medical diagnostics, along with its effect on patient outcomes.
The Power of Machine Learning to See What Humans Cannot
Several Machine Learning for healthcare technologies is available worldwide. These technologies support medical professionals and provide multiple advantages like early disease identification, consistent medical data analysis, and better healthcare access.
To provide a clear picture of the Power of Machine Learning and applications of machine learning in healthcare in transforming healthcare services, we have identified five real-world applications of machine learning that benefit diagnostic labs and patients. These five applications can improve operational accuracy and help strengthen the commercial relationship and patient trust.
1. Disease Diagnosis
Diagnostic laboratories are using AI and machine learning algorithms to discover and diagnose diseases precisely and quickly. Medical imaging analysis is one of the most effective applications of machine learning in medical field. Machine learning algorithms in healthcare can analyze medical imaging data from X-rays, MRIs, and CT scans to spot abnormalities. Also, disease symptoms that are not immediately apparent to the naked eye.
Example: Machine-learning models are used to evaluate hundreds of mammography images to find patterns and traits with regard to cancerous tumors. The algorithms of ML in medical diagnosis can determine whether a new mammography image will likely show signs of cancer.
2. Predictive Analysis
Machine learning algorithms in healthcare can also analyze patient data to predict the likelihood of a patient developing certain conditions or diseases in the future. This process is known as predictive analytics. Predictive analytics can identify patients who are at high risk of developing certain conditions. Such as diabetes or heart disease, so that doctors can take preventative measures to reduce their risk.
Example: To find patterns and risk factors linked to specific diseases, applied ML evaluates vast patient data, including medical records, test findings, and lifestyle data. This algorithm of machine learning can subsequently use these details to forecast a patient’s future health.
3. Drug Discovery
Machine Learning algorithms in healthcare can help in drug discovery, find new drug candidates, and forecast their effectiveness and safety. Conventional drug development techniques frequently require trial and error and can consume time and money. Applied ML can speed up drug discovery by examining vast amounts of data on pharmacological molecules, their characteristics, and interactions with various bodily targets.
Example: Analyzing data from sources such as chemical databases, scientific literature, and clinical trial data, ML models identify patterns and predict the effectiveness and safety of new drug candidates. For instance, machine learning models predict the efficacy of a new drug for treating Alzheimer’s disease based on its chemical structure and how it interacts with the brain.
4. Disease Surveillance
An algorithm of machine learning can monitor and track disease outbreaks. This method is known as disease surveillance. Machine learning models can analyze data from various sources, including social media, news reports, and electronic health records. This identifies patterns and signals that may indicate a disease outbreak.
Example: Machine Learning algorithms in healthcare monitor the spread of viruses. The algorithms examine social media data, including tweets and Facebook postings, to spot flu activity and symptoms trends. The algorithms also utilize this data to forecast the spread of viruses. This also gives public health officials real-time updates so that they can react to outbreaks more swiftly and successfully.
5. Medical Device Development
Companies are already using ML in medical diagnosis to design and improve medical devices, such as sensors and diagnostic instruments, to increase their precision and effectiveness. Applied ML models help labs analyze health data and improve the patient experience. Medical labs can also white-label wearable devices to provide enhanced health parameter tracking.
The real-world applications of ML in healthcare are more comprehensive than we can imagine. The broader role of machine learning in healthcare technology is visible from the above applications. Multiple areas in laboratory medicine shall see steady growth in implementing ML models with more research and use cases.
Machine Learning in medical field revolutionizes diagnostics by empowering medical professionals to identify patients’ conditions more precisely and create individualized treatment plans. Furthermore, consistent research and development are pivotal in giving broader perspectives on various advantages of machine learning in medical diagnostics. Let’s review some recent research and studies on healthcare machine learning.
Importance of machine learning in healthcare: A Review of Some Researches
- A study by Yang et al. (2021) developed a deep-learning model to detect breast cancer from ultrasound images. The model achieved an AUC of 0.93, which is higher than that of radiologists.
- A study by Tahir et al. (2021) developed a machine-learning model to predict the severity of COVID-19 disease using clinical and laboratory data. The model achieved an accuracy of 85% in predicting severe cases.
- A study by Kumar et al. (2021) used deep learning models to detect diabetic retinopathy from retinal images. The model achieved an AUC of 0.94, comparable to that of ophthalmologists.
- A study by Yu et al. (2021) used ML models to predict the risk of heart failure using electronic health records. The model achieved an AUC of 0.87 in predicting the risk of heart failure within a year.
- A study by Hou et al. (2021) developed a machine-learning model to predict the prognosis of patients with hepatocellular carcinoma. The model achieved an accuracy of 0.77 in predicting the survival of patients.
The algorithm of machine learning is such that the statistical models improve the performance of computer systems based on experience with a specific task.
Machine learning in medical field can analyze medical imaging data, predict patient conditions and monitor disease outbreaks in healthcare. It can also improve medical diagnostic technologies, improving healthcare outcomes. Machine Learning for healthcare technologies can empower medical professionals to make more precise diagnoses and create personalized treatment plans.
Research shows machine learning diagnosis effectively detects breast cancer, diabetic retinopathy, and heart failure risk, and predicts COVID-19 severity. This highlights ML’s potential to save lives and improve healthcare outcomes. The benefits of implementing AI and ML in healthcare are evident from the diagnostic advancements recently seen in healthcare. We will publish our next article that provides insight into the world of data analytics. The article will explain how data analytics is transforming the world of medical diagnostics.
We now have a better understanding of artificial intelligence and machine learning for healthcare technologies. As data is critical to the functioning of AI and ML in healthcare technologies, we will soon release our next blog on ‘Data Analytics’. The blog will cover how data analytics is responsible for better patient outcomes and optimized workflow in healthcare.