Artificial Intelligence (AI) and Intelligent Augmentation (IA) in pharmaceutical industry present with a real opportunity to do R&D differently. There needs to be a fundamental shift in drug discovery and AI and IA both holds the key to bringing revolution to the pharma industry into the 21st Century. The current drug discovery process needs to shift dramatically in order to meet the needs both of society and patients. AI and IA both can operate more efficiently and substantially and improve success at the early stages of drug development.
Most of the pharmaceutical organizations are already using some form of automation or maybe even artificial intelligence algorithms. And, this process has generated greater speed and flexibility by modernizing core enterprise systems and thus been has helped in cost-saving. In the competitive healthcare environment, pharmaceutical companies differentiate themselves and their products by demonstrating improved patient health outcomes.
The most critical part of the process is to have reliable data related to population health statistics, total cost of care for certain disease states, and medication compliance patterns. Digital process incorporation can play an important role in more strategic uses of data and analytics (D&A). The technology provides companies with real-time access to Big Data as it moves between systems. Cognitive computing is helping healthcare and life sciences companies make clinical decisions on a dramatically accelerated timeline. Further, it boosts greater accuracy than human labor, removing the problem of human error from data entry and handling.
For example, Artificial intelligence process includes basic robotic process starting from micro base applications running automatically, data collection, process mapping and this all process together help to manage a complete workflow thereby increasing efficiency and reducing time.
In case of intelligent augmentation, the process runs much smoother and faster as it both guided by digital technology and human intervention. The whole process is cognitive, it includes natural language recognition, self-optimization and self-learning with digestion of big data which is on later part used for predictive analysis and evidence-based learning.
Sometimes Artificial intelligence is not well suited to situations where goals and inputs are not well defined, there may be cases where for a particular clinical case efficient data is not available in such case intelligence augmentation will continue to play a major role.
The pharmaceutical companies have been using robotic technology for manufacturing process. However, since last few years the robotics ecosystem has matured and grown. Companies have evolved from robotics projects focused on more finite models where digital technology is core for the drug development process and clinical trial models, and so they require both business and information technology-oriented perspectives. As digital technology is used in more critical operations, it must be guided with appropriate governance and risk management.
AI-based systems are the best use of technology in pharma industry. They can help make sense of this Big Data by creating contexts out of information emanating from different systems. But as far as artificial intelligence can go, because what comes next requires human intervention – and that’s where intelligence augmentation still picks up the race.
The AI vs. IA war isn’t a war after all. They both have an important role to play in future of pharmaceutical industry.
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