AI in Pharma and Biotech Market: North America to Capture Major Share of Global Market

AI in Pharma and Biotech Market: Introduction

Artificial Intelligence (AI) in pharma refers to the system of interconnected and automated technologies in the biotech industry that can function autonomously, with little or no human intervention. AI is an emerging technology that is being applied in numerous facets of the pharma sector, ranging from drug development to diagnosis and even patient care.

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Artificial intelligence use in pharmaceutical technology has been increasing for the last few years. Therefore, the use of technology can save time and money, while providing a far better understanding of the relationships between different formulations and processes parameters.

Key Drivers, Restraints, and Opportunities of AI in Pharma and Biotech Market

Adoption of artificial intelligence (AI) in the drug discovery market in developing regions such as APAC is increasing, thanks to the expansion of pharmaceutical industries by collaborating with other industries and rise in demand to reduce drug discovery time. Postponement of the expiry of a patent is anticipated to further boost the market. Furthermore, the expansion of biotechnology industries is estimated to offer significant opportunities for the market.

An estimated 50% of U.S. citizens are affected by at least one chronic condition; consequently, the pharmaceutical segment of the healthcare industry is projected to be positively impacted by the expected trend of AI adoption.

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All top 10 so-called giant pharma companies (namely Novartis, Roche, Pfizer, Merck, AstraZeneca, GlaxoSmithKline, Sanofi, Abbvie, Bristol-Myers Squibb, and Johnson & Johnson) have either expressly collaborated with or acquired AI technologies to take advantage of the opportunities AI brings to the table. The positive news is that AI, along with Machine Learning and Big Data, has great potential to reduce costs of new drugs and R&D spend both, which for these 10 biggest pharma firms is close to US$ 70 billion, annually.

Introduction of a brand new pharmaceutical drug to plug takes about 12 years and may cost billions in R&D expenditures. Industry leaders are presently focused on the development of more efficient methods, and machine learning is emerging as a possible solution.

The current machine learning initiatives of the top five pharmaceutical and biotechnology companies reveal trends within the following areas:

Digital coaching solutions – Utilization of web-based technologies to simulate one-on-one coaching sessions so that all patients can have the benefit of a uniquely devised medication program.

For instance: Johnson & Johnson’s Patient Athlete program

Personalized medicine – Personalized medicine, also called precision medicine, maybe a medical model that separates people into different groups—with medical decisions, practices, interventions, and/or products being tailored to the individual patient supported their predicted response or risk of disease.

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For instance. Johnson & Johnson, Pfizer, and Novartis’ app of IBM Watson

Acquisitions galore – Pharma giants haven’t historically been flush with data science and AI talent, and as new startups combine the globe of AI and healthcare, acquisitions are likely to still feed to innovation needs of huge (and old) biotech firms mentioned earlier.

Drug discovery – Doctors may feel threatened by the arrival of an AI tool with the aim of “augmenting” their abilities. Unlike doctors, pharma companies have every reason within the world to adopt the foremost cutting-edge technologies within the expensive and lengthy process of drug discovery. Unlike other applications within healthcare facilities, drug discovery seems to own a clearer path to adoption.

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Recently, robots became more collaborative with humans and are more easily trained by moving them through the desired task. They’re also becoming more intelligent, as other AI capabilities are being embedded in their ‘brains’ (really their operating systems). Over time, it seems likely that an equivalent improvement in intelligence that we have seen in other areas of AI would be incorporated into physical robots

Surgical robots initially approved in the U.S. in 2000, provided ‘superpowers’ to surgeons, improving their ability to examine, create precise and minimally invasive incisions, and stitch wounds. Six important decisions, however, are still made by human surgeons. Common surgical procedures using robotic surgery include gynecologic surgery, prostate surgery, and head and neck surgery.

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North America to Capture Major Share of Global Market of AI in Pharma & Biotech

North America is expected to account for a major share of the global market of AI in Pharma and Biotech due to the high prevalence of chronic diseases among the population. Moreover, well-established healthcare infrastructure, increasing research activities, and the hub for key industry players are major drivers of the adoption of artificial intelligence for pharmaceutical companies in the region.

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