New Approaches to Patient Treatment: 15 Use Cases of Machine Learning in Healthcare

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Healthcare is one of the most important industries in the world, bearing a tremendous responsibility over people’s well-being. Decreasing the margin of error and making treatments more precise can save lives — but to do that, the industry needs to evolve.

Artificial intelligence (AI) is the next step for healthcare. Many organizations already use it to some extent, and this trend doesn’t look like it will end any time soon. Recently, one particular type of AI has gained significant traction — machine learning (ML). Let’s talk about it.

In healthcare, machine learning models rely on either research or patient data, depending on AI’s purpose. Various machine learning techniques can help sort and categorize data to discover patterns in datasets, which can lead to new insights. Because of this, ML models are applicable to practically any major task in the healthcare industry.

We’ve divided machine learning projects for healthcare into four groups: research, diagnostics, treatment and medical paperwork. Here is how it’s used in each group.

Machine Learning in Medical Paperwork

When people speak about the healthcare sector, they tend to focus on medical diagnostics and treatment. However, administrative processes are another important aspect of the healthcare workflow. Let’s look at how machine learning algorithms can help out with paperwork.

Hospital Management Optimization

Growing demand for healthcare services often leads to more paperwork. Luckily, machine learning models can help.

Most tedious processes that take up a lot of employees’ time can be automated. For example, inventory management can be left to AI, since it can analyze inventory stock and automatically request missing items.

Another benefit of AI comes in staff scheduling. Instead of doing it by hand, AI can automatically go through each employee’s working hours and create an optimized work schedule.

Health Insurance

Most companies are still using manual processes to create insurance plans for people based on their medical history; however, with machine learning, it’s a thing of the past.

Instead of manually reviewing patient records, you can leave this task for the AI, which can automatically go through a patient’s records and develop an insurance plan covering the most probable causes of health risks. As a result, work that previously could take days will be done in minutes.

As a neat bonus, machine learning’s pattern recognition can assist in fraud detection, and even detect false positives. Health insurance automation saves time and money from potentially fraudulent activities.

Supply Chain Management

One of the most persistent problems in the healthcare sector is supply chain disruptions. While not something new, past methods of addressing them usually fell short, creating shortages. Today, that has all changed, thanks to AI.

ML models can automate many tasks regarding paperwork that previously had a high margin of human error — consider the case of contacting suppliers and planning.

Instead of relying on humans to contact suppliers and search for the best deal, you can leave this task for AI to do automatically. Higher operational efficiency leads to higher profits and less chance of going out of stock in critical moments.

Prescription Audit

Prescription errors happen mostly as the result of human error, or rare cases of two patients with identical names and diagnoses. While this might seem insignificant, it can lead to serious patient outcomes and, in some cases, losing money on insurance.

To address this issue, some healthcare and wellness providers have adopted machine learning. Firstly, it eliminates human error, since ML algorithms are more vigilant and attentive than humans.

Secondly, by analyzing patient records, AI can find the perfect prescription for every patient that will have minimal side effects. This is important because some prescription drugs can worsen conditions if the patient is allergic to any of the drug’s ingredients.

Machine Learning Projects in Diagnostics

The faster a diagnosis is made, the better the odds for positive patient outcomes. Machine learning models can help out with this task by spotting early warning signs of diseases. Here are a couple of examples of how it works.

Pathology Warnings

Recently, researchers have been combining machine learning capabilities with medical imaging to make the diagnosis process more accurate.

They utilize fine-tuned ML models trained on medical images based on bodily fluids’ appearance. By combining machine learning with computer vision, software can detect deviations in real-time.

At this point, such systems are still in the early development iterations. Therefore, they rely on human medical expertise to verify and validate the machine-made analysis.

Early Detection of Rare Diseases

Since human medical professionals can’t possibly hold the symptoms of every known illness in their head, they require outside help for disease diagnostics.

Engineers can accurately detect diseases by training models on descriptions of every known disease and its symptoms. But what about diseases that have similar symptoms?

To avoid false positives, ML models collaborate closely with human clinicians. However, given the rapid advancements in machine learning, it’s likely that AI will eventually operate independently.

Predictive Analytics

The best way to save the lives of high-risk patients is to prevent complications from developing. Disease detection through medical images at early stages is possible, but their reliability still involves guessing. Machine learning tips the scales in our favor.

ML algorithms can analyze patient records and medical imaging to detect risk factors and predict the likelihood of conditions or diseases. This approach works best with computer vision for even broader patient analysis.

The analysis results in the prediction of complications, and ways to mitigate them. This way, ML models can enhance patient outcomes and save money and time for a potentially complicated treatment.

Oncology Detection

When it comes to cancer research and treatment, speed is everything. Early detection of cancer cells guarantees better patient outcomes. In recent years, researchers around the world have started working on machine learning projects for oncology detection.

With the help of AI, medical experts can detect cancer in patients at very early stages, effectively increasing their chances of surviving. While this implementation is relatively new, it has already shown promising results by saving the lives of numerous cancer patients.

Machine Learning in Treatment

In this category, machine learning projects take the role of clinical decision support. They provide doctors and patients with information about the best course of action for the patient’s health. It is common to see solutions that combine machine learning techniques with deep learning or natural language processing (NLP). Here are some examples.

Patient Behavior Modification

One of the best ways to keep patients healthy is to prevent them from developing bad habits. However, such habits tend to be hard for patients to break on their own. This is where machine learning comes in.

ML models can analyze patient data gathered in real time through wearable tech and generate insights or helpful tips. This creates support for patients who try to quit smoking, for example.

Virtual Nursing

Some medical centers experience problems with the ratio of nurses to patients. This is another task that machine learning can help with.

By utilizing ML models, businesses can create remote patient monitoring systems with virtual nurses who tend to their patients through their phones. They can schedule appointments with doctors, answer questions, and show empathy while communicating with patients.

Robot-Assisted Surgery

Since healthcare always strives for perfection, the introduction of machine learning has the power to mitigate even the narrowest margin of error in hospitals.

Robots powered by machine learning can be valuable assistants for medical professionals. With their deep learning algorithms, such robots can help surgeons perform complex procedures more precisely, especially when it comes to microsurgeries. A great example is neurosurgery, where one wrong move can determine the operation’s success.

Mental Health Analysis

The growing attention to mental health highlights the need for an effective healthcare system that can identify early signs of mental health problems, and recommend effective treatment.

In this pursuit, machine learning has emerged as a valuable tool. The ML model conducts psychological tests and analyzes medical records to detect early signs of anxiety, depression, or, in more severe cases — schizophrenia.

Machine Learning in Medical Research

Machine learning models excel at pattern discovery — and if they are trained on previous research papers and articles, they can provide scientists with helpful insights. Here are examples of machine learning projects for pharmaceutical companies.

Drug Discovery

The drug discovery process requires both a lot of creativity and precise knowledge of how chemicals interact. These are exactly the characteristics that machine learning algorithms have.

ML model’s deep learning capabilities make it a perfect discovery tool. How? By knowing existing drugs and their effects, machine learning algorithms can create new chemical combinations based on the side effects of already-known medicine. Another benefit of using AI is its ability to conceive new drugs during compound or substance testing.

ML models make drug discovery as straightforward as it can be! Healthcare professionals feel the results — the drugs created in this way tend to be cheaper and improve patient outcomes.

Clinical Trials Research

Machine learning can speed up clinical trials in various ways. For example, the model’s deep learning algorithms can optimize protocols to mitigate redundancies. To do that, the model needs a dataset containing historical clinical trial data, and knowledge of tested medicine.

Faster clinical trials means getting new medicines to patients faster, cheaper, and with potentially better patient outcomes.

Genetic and Genomic Research

The medical practice of genetic and genomic research contains a lot of complex data. While it might be hard for humans to comprehend, it’s no problem for machine learning.

With AI’s ability to analyze large amounts of data and the ML model’s pattern recognition, healthcare has become a perfect insight-generating machine. It enables researchers to collect data on a cellular level and identify cell types.

The implications are huge — from more effective cancer treatment to personalized medicine. By understanding the information in a patient’s cells, healthcare professionals can identify potential disease risks and take appropriate actions to mitigate them.

To summarize, machine learning is taking the healthcare industry by storm. Automated paperwork, improved patient outcomes, research, and drug discovery assistance — the applications are limitless! However, just as every rose has thorns, every technology has its obstacles. So, what to expect?

Continue reading our original article to learn more about machine learning challenges in healthcare, future AI advancements, and promising market stats.