When one tries to publish in an international journal these days, they inevitably encounter an obstacle: researchers around the world have more than likely published thousands of papers on a similar topic, which could need an entire year to read through and database. And this problem is not only for scientists only. Any company or individual looking to do original or even review research has to go through thousands of pages of literature/journal articles to write one literature review.
The research community may need some time to get used to artificial intelligence in a literature review, but as long as the need for service and tailor content is there, the use of AI should keep rising!
To define AI with a couple of sentences would be pretty difficult. But, in short, Artificial Intelligence (AI) is the concept of machines being able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making. It is not a new concept anymore. Artificial intelligence is everywhere.
Whether it is Siri or Alexa, or Google, we use AI every day; sometimes without realizing it ourselves. However, AI is not just limited to what you can do on your smartphone or computer. The technology also has a wide variety of applications in the business world.
The use of AI systems in data analysis is not new either, since the function of AI depends on collecting relevant data and analyzing data sets. However, in clinical research, the use of deep learning is still new and much unexplored.
AI literature review?
An artificial intelligence (AI) system is much faster and infinitely more efficient than a human being at scouring the literature to identify articles relevant to a medical device’s certification.
An AI system can be trained to understand clinical evidence’s complexities and anticipate what’s required for a particular type of medical device. This has enormous implications for the time it takes to review and certify new products, especially novel or highly specialized ones.
Consider the case of an orthopedic surgeon developing a new shoulder implant. There may be very little information available on this type of implant in the current literature, so it is not uncommon for it to take six months or longer to review all of the research.
The surgeon could spend thousands of hours in his free time identifying papers from other fields that might apply. However, he would miss many important papers, and there would still be no guarantee that he’d found everything relevant to product certification.
It would be almost impossible for any human being to do this kind of research as thoroughly as an AI system with an excellent neural network could.
Benefits of using artificial intelligence: Time and effort
When studying the medical device certification process, researchers have traditionally conducted a systematic literature review by going to a medical database and searching for articles with keywords.
A human then has to read through every single article that comes up that might be relevant to the question at hand. This can be a time-consuming and sometimes frustrating task. However, when we use artificial intelligence or machine learning, there is no need to pore over every article.
The beauty of machine learning is that it can “learn.” So, the more “input” you give or, the more you use it, the more specific the results become.
For example, the artificial intelligence system is trained on thousands of data sets from previous studies, so it knows exactly what questions researchers are trying to answer and what kind of data they need.
There is no need for manual data entry or individual statistical methods. This allows it to create an accurate model for how likely an article contains information pertaining to the study’s topic.
In the past, keyword search queries were often too broad, resulting in a large number of irrelevant articles being included in the search results.
If given the correct inclusion and exclusion criteria, artificial intelligence works better than any individual. The AI systems can automatically extract key phrases from scientific papers and group those papers into similar categories based on their content. This saves the researcher time because it automates searching for relevant articles and later organizing them into groups to read more deeply.
Another advantage is that it helps reduce costs associated with hiring experts and training them to perform better at their jobs.
The ease of using machine learning algorithms
Although machine learning is promising, the name itself sounds complicated enough that most people may shy away from it.
The truth is, adopting AI for literature review stems from the fact that it is effortless to use and does not require any programming skills. Also, since most researchers are familiar with computers, an AI literature review does not require a lot of training time.
The main goal of AI is to automate research method processes that are currently done by humans. Machine learning algorithms are a subset of AI. They learn from data, find patterns in it, and make predictions.
With the right software, one that combines several different methods of data collection, analysis, and reports all into one platform, it should be as easy as typing a few words in a search engine.
What is RPA?
Robotic process automation (RPA) in life sciences industry allows organizations to automate tasks just like a human being was doing them across applications and systems. Although RPA is referred to interchangeably with software robotics, RPA does not involve physical robots or artificial intelligence (AI). Instead, RPA tools are trained to perform specific tasks by capturing user actions via the user interface (UI). Hence, their use is more in doing medical surgeries than searching about them.
Can AI write a systematic literature review?
The short answer is yes. But the longer answer is that, in many cases, it can do this with little guidance from the user. With the AI technologies that are available in the market now, it will be difficult to write a complete systematic literature review with AI only.
What are the types of artificial intelligence?
It is a tough question to answer because AI takes so many different forms and serves so many purposes. Below are some of the most common types of artificial intelligence:
- Reactive Memory
- Theory of Mind
- Self Aware
- Limited Memory