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Process Automation involves a type of AI, which relates to the automation of what one can broadly call back-office function. It is installed through more efficient handling of digital and physical tasks using technology.
Usually, research studies suggest looking at Artificial Intelligence (AI) through the lens of business inclinations rather than technologies. There are three types of AI being deployed by life science businesses that can present a framework for enhancing efficiencies; find them below.
This type of AI relates to the automation of what one can broadly call back-office function. It is installed through more efficient handling of digital and physical tasks using technology.
• Shifting the data from a site to storage or relocating the data from a call or email into a record storing system.
• Reconciling failures in systems or data by merging and automatically checking the information in multiple designs and document types.
• Reading radiology or clinical reports through Natural Language Processing (NLP) techniques to extract provisions, data points, and eventually making conclusions.
AI-Driven Data Analysis
This category of AI is often feared by specialists and denoted as better decision making or substituting a human in making a decision process, which is not the truth. The AI-driven methodologies in clinical research are usually designed to aid the human to make a decision. One can broadly separate the AI method into semi or fully automated. An instance of a fully automated AI driven method would be if someone takes an MRI scan, and when the scan gets processed by the software, one can know if the patient has a particular disease or not.
A majority of the methods are semi-automated and will need either the initial input or the final decision made by an expert. In contrast, the AI algorithm will handle everything in between in a fully automated manner.
AI and Insight into the Data
Here one will find a blend of machine learning techniques and true AI. Examples include predicting a specific cancer type based on a combination of genes, computerization of personalized targeting for the success of a particular therapy, picking the right dosing based on the treated patient data, and much broader.
In related markets, this type of AI will notify the insurer with more detailed actuarial modeling, update drug discoverer of most likely combinations to succeed and fail, logically influence the design of the next trial based on the competitive intelligence, and use endpoints and patient populations.