Technology has come a very long way in the last decade, from only applying it to some basic tasks, such as computing and capturing data. Today, technology has made some incredible breakthroughs, particularly in the medical field, as it is used in almost every aspect of healthcare. Let’s take another look at one of the most innovative forms of technology – Artificial Intelligence (AI), and learn how artificial intelligence in medicine has evolved.
Artificial Intelligence in Medicine Is the Future
What Is Artificial Intelligence in Medicine?
Artificial intelligence in medicine is referred to as the use of AI automated processes or technologies in diagnosing and treating patients. This might seem like a simple and easy task, and anyone could do it with ease, but various processes and procedures need to be done in the background, such as:
- Gathering data from patients through surveys and tests
- Analyzing and processing the results
- Using various data sources to get an accurate diagnosis
- Determining and presenting options for treatments
- Administering and preparing the selected treatment
- Monitor and provide patient aftercare while arranging follow-up appointments
A study from 2017 shows that physicians and clinicians spend a lot more time on desk work and data entry than engaging and talking to patients. And thanks to artificial intelligence in medicine, healthcare professionals can deliberately and sensibly free up time and effort.
Faster Drug Development
Drug development is a notoriously time-consuming and expensive process in which a lot of resources are needed. However, many of the analytical processes can be much more efficient with the help of AI and machine learning, which potentially shave off hundreds of millions of funding and years of hard work.
Improve Gene Editing
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), especially the CRISPR-Cas9 gene-editing system, is a significant leap forward in our capacity to alter DNA cost-effectively and precisely.
The technique depends on short guide RNAs, also known as SgRNA, to locate and edit a specific DNA location. However, the guide RNA can fit in various DNA locations leading to unwanted side effects. That’s where machine learning and AI technology comes in; they have proven to produce the best results when predicting the degree of off-target effects and guide target interaction for a certain sgRNA.
As a result, it significantly shortens the processing time and development of guide RNA for every human DNA region.
To correctly diagnose diseases takes doctors years of learning, experience, and medical training. Even then, the diagnostics can be time-consuming and arduous. The need for experts in many medical fields far exceeds the currently available supply putting healthcare professionals under stress. Therefore, in some cases, doctors even delay life-saving patient diagnostics.
Machine learning, deep learning algorithms, in particular, have enormous advances in diagnosing diseases automatically, making them cheaper and more accessible.
Here’s How Machines Learn to Diagnose
Machine learning algorithms can learn to recognize the pattern in a way similar to how doctors see them. The major difference is algorithms require plenty of concrete examples to learn. Besides, they have to be orderly digitized for machine learning. This makes it exceptionally helpful in aspects in which the diagnostic data from the doctor has already been digitized, such as:
- Classify skin lesions in skin images
- Seeking diabetic retinopathy markers in eye images
- Detecting strokes or lung cancer on CT scans
If the algorithms are provided with enough accurate data, it can be just as good at detecting diseases and making diagnostics as experts. What’s more, it only takes a fraction of a second to do it, which makes all the difference in the medical field, and it can be inexpensively reproduced all around the globe.
Treatments and responses to drugs vary from person to person. So personalizing treatment to fit each patient’s schedule and drug preferences can be a massive help in saving many lives and increasing their health condition.
Artificial intelligence in medicine can make quick work of the complicated statistical work with ease in a matter of seconds and help discover which features suggest that a patient may have a clear reaction to a particular treatment. So the algorithm can predict a patient’s likely response to a specific treatment. The algorithms learn this from cross-referencing similar patients then compare their treatments and outcomes.
Artificial intelligence in medicine has helped us out a lot, from developing new drugs, diagnosing disease more quickly, and personalizing treatment for patients to edit DNA to improve health conditions for later generations.
This is just the beginning, and there is limitless potential for what technologies can do. If we know how to implement artificial intelligence in medicine correctly, we can potentially save countless lives and improve the lives of many.
Let us know your opinions on the future of artificial intelligence in medicine in the comment section below!
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