The integration of AI in various industries is proceeding swiftly, with enterprising companies finding new ways of leveraging the technology. One field in which it has proved rather effective is the healthcare industry.

From medical imaging to analytical diagnosis, AI is improving healthcare technology in unprecedented ways. Here is an overview.

Embedded Computers in Healthcare

Before we dive into how AI is contributing to the medical industry, let’s look at the role played by embedded PCs in the first place. Contrary to what you might expect, computers are used in virtually every activity in the medical field. 

Medical imaging is the most obvious use case. Powerful computers power the imaging machines and process the results, allowing doctors to make accurate diagnoses. 

Then there are medical tablets, used by healthcare professionals in performing their duties. Handheld tablets can be used to keep track of patients’ medical records or interface with diagnostic tests to view the results immediately. 

And finally, embedded PCs are used in the bevy of administrative tasks found in any healthcare facility. This includes billing, keeping track of the complex stock of medical supplies, as well as managing the massive store of records.

What Does AI Bring to the Table?

We had discussed the functions performed by a medical computer, but how can AI help in any of these activities?

Improved Imaging

Traditionally, medical imaging relies on trained healthcare professionals to study the tests and draw conclusions. While this is still the case, AI image processing can quickly analyze the tests to give a fairly accurate diagnosis itself.

This has come as a boon to speed up the treatment process and improve diagnostic accuracy, as doctors only need to verify the AI’s conclusions.

Predictive Analysis

AI has been built on statistical analysis models, and as such, it is very good at analyzing large datasets to provide insights. This ability is crucial in preventive healthcare.

AI models can be used to sift through a patient’s medical history to predict major risk factors, helping create a more personalized treatment plan.

Patient Interaction

Digitally booking appointments has become the norm, but there are slight issues with the approach. Patients are often not sure about what tests or consultations to book, requiring human assistance. 

But AI chatbots can take over this role, guiding patients to the correct process based on their reported symptoms. These virtual assistants can even provide limited medical advice to deal with minor symptoms.

Why Is AI So Useful in Healthcare?

The basic principle behind AI algorithms is statistical analysis. Machine learning works by going through massive amounts of data, finding correlations and trends. This lets it predict the possible conclusions when presented with other, smaller datasets. 

In healthcare, this technology is instrumental. Healthcare is an exact science but with a deluge of information to sort through. The pertinent information is often buried underneath a host of unrelated data, making it difficult for a person to unravel. 

For example, diagnostic tests can reveal the chances of various diseases. Normally, a doctor has to rely on their experience and judgment to make an accurate diagnosis, without any guaranteed method of verifying how likely it may be. 

However, an AI algorithm can be trained on a database of similar test results to accurately pick up on the subtlest of symptoms to predict the likely diagnosis. While a medical professional would still be required to confirm it, this reduces the uncertainty and weeds out false positives.

Does AI Need Different Hardware Too? 

Embedded computers have been deployed in healthcare applications for ages, but can these PCs tap into AI algorithms as well? Or do facilities need to invest in new computers as well?

The answer, as always, is that it depends. Quite a few of these AI solutions are cloud-based, which means the specifications of the end computer on which you run these apps do not matter. 

But this raises concerns about privacy and data security, and many healthcare providers dislike the idea of sharing patient information with a third-party server. In that case, there is a need for a GPU-powered PC to run these AI algorithms on-premise.

So is AI the New Norm in Medical Computing?

To an extent, yes. AI improves the efficacy of diagnostics across the board, besides being able to analyze patient data to improve preventive care as well. This makes AI technology essential in the medical field. 

And these are still the early days. As AI develops further, more life-saving applications will be found, including in research and drug testing. 

This is also one type of AI usage that isn’t controversial, as instead of replacing workers it will be helping save lives more effectively. Needless to say, we are going to see more integration of AI in the healthcare industry.