With the introduction of 'Evinova', AstraZeneca looks to utilize AI for making clinical trials more efficient and effective.

AstraZeneca announced it has established Evinova This division is wholly focused on bringing cutting-edge digital solutions and AI into the clinical trial landscape. AstraZeneca believes this initiative could significantly lower both the costs and timelines associated with clinical trials.

Evinova will concentrate on implementing globally recognized digital technologies already used within AstraZeneca to improve the planning and execution of clinical trials. Their aim is to expedite a process that usually stretches beyond seven years, especially since an alarming 80% of trials don't meet their recruitment targets. clinical trial The new division plans to combine AstraZeneca's extensive clinical and regulatory knowledge with deep digital technology expertise, covering strategy and development, digital product creation, data science, AI, and even behavioral science for user experience design.

Evinova will not only focus on digital solutions but will also provide tailored scientific services, which include remote patient monitoring, trial design, and consulting. Notably, the division has already formed collaborations with two major global clinical trial organizations, Parexel and Fortrea.

Pharmaceutical Companies Are Leveraging AI to Enhance Clinical Trial Outcomes

Top pharma industries are harnessing technology

to quicken the patient identification process for clinical trials or to minimize participant requirements, thereby accelerating the drug development timeline and achieving substantial cost reductions. artificial intelligence The human trials stage is notably the most demanding in terms of resources and time

and involves several years of patient recruitment and testing of new medications. This phase can incur expenses reaching over a billion dollars, from the initial drug discovery through to completion. drug development Key industry players like Amgen, Bayer, and Novartis are utilizing AI to analyze extensive datasets, including public health records, prescription information, insurance claims, and their proprietary data – all with the aim of significantly reducing the time needed for trial participant enrollment.

is employing AI technology to drastically decrease the number of participants needed for the late-phase testing of asundexian, an innovative drug aimed at lowering the long-term risk of strokes in adults.

German pharmaceutical company Bayer By correlating results from mid-phase trials with real-world data from millions of patients in both Finland and the U.S., Bayer utilized AI to estimate long-term risks in a similar population. This allowed the pharma company to launch the late-stage trial with fewer participants than anticipated. Without this technology, Bayer expected it would have faced considerable increases in costs and an extended timeline of up to nine months for volunteer recruitment.

AstraZeneca reports that the digital health market is currently expanding at an impressive annual rate of about 14% and is expected to exceed $900 billion by 2032.

This growth is largely driven by advancements in research solutions and remote patient monitoring, alongside contributions from activities such as screening, diagnostics, disease prevention, and digital pharmacies.

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