In the wake of digitisation, Artificial Intelligence (AI) has taken a leap in the pharmaceutical industry ranging from discovery to go to market. It has a wide-reaching potential in the pharma industry such as research & development, clinical trials, marketing and sales analytics. Using a machine learning programme can reduce the time spent on examining data, saving money and allowing researchers to focus on other issues.
Traditionally, once a company decides to commercially manufacture a drug, it discards hundreds — if not thousands — of trial batches, before it can zero in on what is termed a ‘golden batch’. In an era of digitisation, this process can be hastened by using AI to rapidly identify this one ideal batch and manufacture it continuously. While technology allows these manufacturers to operate far more efficiently, this aggressive investment in building advanced digitally-led manufacturing operations also enables drug-makers to be more compliant, since multiple manual checks and balances these companies had in place are being replaced by faster AI and machine learning (ML)-driven processes.
Latest researches have shown that the global pharmaceutical industry spends a significant amount of revenue on Research and Development (R&D) activities that are failing to deliver projected returns but that digital transformation with Artificial Intelligence (AI) has been emerging as a harbinger towards enhancing R&D productivity and effectiveness and creating interest among industry leaders to opt it. In fact, the market volume for AI-based medical imaging, diagnostics, personal AI assistants, drug discovery, and genomics is projected to reach $10B by 2024.Artificial Intelligence arms the Medical Representatives (MRs), and the plan gets even further refined. The AI program can take into account near real-time customer interactions and data, and use that information to recommend immediate “next best actions” for Sales Representatives. For example, the AI system might suggest that a rep contacts a particular doctor soon because the doctor just attended a seminar that relates to the science behind the rep’s product.
Some programs with the help of AI have been created to overcome limitations inherent in conventional computer-aided diagnosis that simulates expert human reasoning. It has been instrumental in solving many of the problems impeding the creation of effective AI programs. Strategies have been developed to limit the number of hypotheses that a program must consider and incorporate pathophysiologic reasoning. The latter innovation permits a program to analyse cases in which one disorder influences the presentation of another. Prototypes embodying such reasoning can explain their conclusions in medical terms that can be reviewed by the user.