top of page


Using AI in the Pharmaceutical Industry

Example uses of AI in the pharmaceutical industry

AI has been improving different workflows and systems within pharmaceutical. In pharmaceutical, it is notably applied in activities including drug development, record keeping, and clinical trials.

Drug Development

In drug discovery, AI expedites the identification of potential drug candidates by analyzing extensive datasets of biological and chemical information, aiding researchers in designing drugs more efficiently and potentially hastening therapeutic development.

Electronic Health Records

Leveraging AI, particularly Natural Language Processing (NLP), transforms the management of Electronic Health Records (EHR) by extracting meaningful information from unstructured clinical notes, simplifying documentation, reducing errors, and enhancing healthcare professionals’ access to vital patient data, thus contributing to better-coordinated care and decision-making.

Clinical Trial Research

AI optimizes clinical trial design, candidate identification, and outcome prediction, streamlining the trial process and potentially accelerating the introduction of new treatments to market, thereby advancing healthcare innovation.

Virtual Health Assistants

AI-powered virtual health assistants, including chatbots, offer diverse services such as answering patient inquiries, providing medical information, and assisting with administrative tasks, operating round-the-clock to enhance healthcare accessibility. By automating routine tasks, healthcare professionals can dedicate more attention to direct patient care, ultimately enhancing the overall patient experience and engagement.

Incorporate AI into your pharmaceutical operations to streamline processes, optimize decision-making, and revolutionize patient care.

How much does it cost for pharma companies to implement AI?

Different AI tools cost differently, and some of them have a "pay-per-use" model. The total cost would depend on the type of AI tool being used and the number of end-users. However, banks should also take into account the availability of base systems and their ability to connect to AI tools. This would also be an additional investment to those who do not have the necessary base systems in place.


Build your next AI-powered software with BlastAsia

bottom of page