Among the most rapidly growing disciplines in the life sciences industry is Artificial Intelligence (AI) and machine learning (ML), and their application is expected to grow even more by 2022.
Fremont, CA: Nearly half of the global life sciences professionals are either using or are interested in using AI in some other work areas. The healthcare industry is very competitive and dynamic. The AI tools pose to be very attractive to this industry as they have been successful in clinical research, trial management, regulatory market access, and commercial effectiveness application. Thus, AI and ML have been adopted into a healthcare company’s analytics’ strategy, outplaces gut instinct and rule-based decision making. It offers evidence-based insights that can unveil sophisticated patterns such as those found in patient behaviors, health outcomes, HCP prescribing, and sales, which were undetected previously. Advancements in AI and ML, coupled with the rising availability of healthcare data, provides the life sciences industry a wealth of insights and the promise of competitive advantage with the power to drive healthcare forward.
Machine learning had first appeared in the 1950s; still, after two decades, the life science industry is finally interested in it because the data storage and data processing capacity has grown exponentially. It has raised to a level where now it is affordable for businesses to use machine learning.
ML extracts from various fields of study: artificial intelligence, data mining, statics, and optimization. Data mining utilizes data storage and data manipulation technologies to prepare the data for analysis. Then as a part of the data mining task, statistical or machine learning algorithms can point out the patterns in the data and make predictions about the new data.
AI and ML can provide previously inaccessible insights that can positively impact commercial activities and support numerous healthcare organizations’ functions. The AI and ML methods have been proven to consistently deliver more accurate outcomes in less time than conventional assessments.
To conclude, it is essential for classical statics and machine learning to co-exist; the utilization of one versus the other must be based on the analytical hurdles. In a few situations, they might serve different purposes while in others, they might overlap. The hour’s question is not to figure whether one approach must be adopted at the expense of the other, but to determine which one is appropriate for any business situation.
See also: Top Machine Learning Companies