Information technologies have leaped like never before during the last decade. Besides basic tasks such as computing and capturing data, more complex tasks benefit from automation. So, let’s examine one of the most innovative forms of technology – Artificial Intelligence (AI). On the one hand, let’s learn how artificial intelligence in medicine has evolved. And, on the other hand, we can explore the question of whether artificial intelligence in medicine is the future.
Artificial Intelligence in Medicine Is the Future
What Is Artificial Intelligence in Medicine?
Healthcare is increasingly using AI automated processes or technologies in diagnosing and treating patients. These technological implementations might seem simple at first. However, various requirements need to be fulfilled, 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 doctors spend a lot more time on desk work and data entry than interacting with patients. Fortunately, healthcare professionals can free up administrative time to engage more with patients thanks to AI in medicine.
Faster Drug Development
Drug development is a notoriously time-consuming effort that consumes many resources. However, AI and machine learning can increase the efficiency of numerous analytical processes. Hence, potentially shaving off fortunes from funding and years of intense labor.
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 (SgRNAs) to locate and edit a specific DNA location. However, these SgRNAs can fit in various locations along the nucleic acid strand, leading to unwanted side effects. This technical challenge can benefit much from machine learning and AI. For instance, they are viable at predicting the degree of undesirable edits. And are also effective at guiding target interaction.
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
By working with good quality data, algorithms can detect diseases and make diagnoses as effectively as experts. Moreover, they provide output in a fraction of a second, making a substantial difference in the medical field. And finally, since these technologies are digital, they can scale easily and be rapidly deployed 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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