The way industries are adopting AI and machine learning, it will eventually drive remarkable growth in the coming years. Research says the AI market will reach $89.8 Billion in Annual Worldwide Revenue by 2025. The pharmaceutical industry is not an exception in this scenario. The impact of Artificial Intelligence in the pharmaceutical industry is undeniable. Many pharma companies throughout the world have already started leveraging AI in their business. Various factors are responsible for the advancement of this cutting-edge technology in the pharmaceutical industry and it has a promising future ahead. Today’s post will discuss everything about this.

1. How Artificial
Intelligence is
playing vital role in
disrupting the
Pharma Industry
PRESENTED BY TYRONE SYSTEMS

2. For example, sales and marketing data
exist on the ERP and CRM systems, but
there’s no easy access to the data.
Subsequently, management cannot make
real-time strategic changes in sales and
marketing. A way in which the company is
leveraging technology is by developing
analytics capabilities that provide senior
management with actionable insights that
deliver quantifiable benefits.
Delivering actionable
insights

3. Patients can now easily share their
information for clinical trials with the touch
of a button. Also, the data captured is
precise, contextual and of high quality.With
the improvement of data capturing
technologies, the opportunity to leverage
qualified data into an AI program will
reduce patient risk and improve pharma’s
quality and time.
Facilitating Clinical
Trial Process

4. Nowadays, with the adoption of AI and
big data analytics tools by the
pharmaceutical industry, generating
hypotheses, validating theories, finding
unexplored targets and biomarkers, as
well as discovering new connections is
faster and easier.
Drugs Research &
Development

5. With AI medical images can be read to
generate accurate diagnoses and
hypotheses about different indications;
with sentiment analysis, unforeseen
side effects of interventions
can be mapped out;
with machine learning, personalized
medicine can finally be possible.
Image Recognition

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