Pros and Cons of Using Machine Learning in Medicine

Long time ago, we didn’t have machines, access to the Internet, and the entire cyberspace. But things are different now—many people are dependent on technology, especially when it comes to communication and healthcare. One thing that experts continuously look into is the use of machine learning in the field of medicine. While they have seen countless benefits from this type of technology, there are also limitations that we need to look into.

Will the advantages of machine learning outweigh its disadvantages? Let’s see.

What Is Machine Learning?

Machine learning is one of the branches of artificial intelligence (AI), wherein systems analyse data. Then, it uses the information and algorithm to predict outcomes even without explicit instructions. It can view trends, learn from them, and make decisions without much need for human intervention.

In medicine, several healthcare providers use machine learning models to draw essential features from a huge amount of data from various patients. With the extracted data, the system will then help detect diseases and predict potential diseases.

Machine Learning in Medicine: The Pros

Timely Data

One of the most important features of technology is its speed. That is why it can generate real-time data compared to the manual way of doing things. Without machine learning, it takes longer for medical professionals to get information about the patients and their respective conditions.

To put it simply, medical professionals can acquire accurate information quicker, which will then help them diagnose and address medical issues. And if there are changes in the patient’s status, doctors can then respond to it.

Helps in Delivering Tasks More Efficiently

Machine learning has brought a revolution to the healthcare industry. From simple tasks like scheduling appointments to tracking the historical health statuses of the patients, AI has made such tasks easier to complete.

Aside from that, machine learning has also enabled medical professionals to identify significant visual markers in radiology technology. With this, they won’t have to spend hours on intense analysis.

Reduces Stress of Healthcare Professionals

Some healthcare professionals are exposed to a stressful environment due to varying factors, such as pressures from deadlines, complicated disease diagnosis, results analysis, etc. Now that the healthcare sector has made good use of technology, it will be easier for them to do their duties without taking much of their time.

Provides Assistance in Research

Research helps professionals seek solutions for people’s problems, especially in the healthcare sector. And AI has been a significant factor that streamlines the data collection process from various sources, which the experts will use for their medical research.

Saves Time and Resources

In summary, the streamlining of the processes will help save the time of every healthcare professional, which paves the way for them to alleviate the sufferings of every patient suffering from a particular health condition. Machine learning also saves resources, especially in the financial sector.

Machine Learning in Medicine: The Cons

Needs Monitoring from Humans

While machine learning decreases the need for human intervention, healthcare professionals still need to carry out patient surveillance. Take, for example, behavioural patterns that a patient manifests, which can only be detected by humans. These changes in their behaviour may be essential in preventing medical complications or helping diagnose another health condition. And without the presence of humans, these may go unnoticed.

Has the Possibility to Overlook Other Variables

The needs of humans don’t only stop with the physical aspect. It extends further into social, economic, emotional, and historical factors. While technology continues to develop, AI is only limited to addressing the physical needs of the patients as of now. Taking these things into consideration, machine learning models may overlook important variables needed in medicine.

May Be Inaccurate

Some illnesses have large datasets because many people are experiencing them. Meanwhile, rare diseases only have limited datasets. The issue here is that medical AI is reliant on the prevailing information, which may result in misdiagnosis if a particular health condition only has little data available.

Risks of Unemployment

Nowadays, we have resorted to the online space for almost everything—from block chains for digital currencies to artificial intelligence for carrying out tasks that humans are supposed to do. Because of that, many people fear that they may lose their jobs. The same is true in the field of medicine if the machines will become smarter.

Problems with Cybersecurity

Scammers and hackers are lurking on the Internet, which may put the security of the gathered data at risk. That is why experts have to create a system that will improve cyber security before it becomes difficult to address.

Should You Use Machine Learning Models?

Machine learning models and other artificial intelligence systems will surely help healthcare providers deliver their services to patients more efficiently. That if these providers know how to use AI the right way. StratHealth Ltd can help you determine the right technologies and strategies that will push you to the top in the healthcare industry. Call us now on 020 8128 4420 or send an email to peter.ellis@strathealth.co.uk.