AI in the Pharma Industry

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The pharma industry’s success at improving the standard of care has actually made the industry’s future more difficult. The effort and expense needed for incremental benefits and added value for patients means diminishing overall return on investments. Implementation of Artificial Intelligence or AI in the pharma industry over the next 10-20 years is expected to provide the opportunity for continued improvements and innovation to keep the industry profitable. Where is AI expected to have the most significant impact in the pharma industry?

Drug Discovery

We all know the statistics. Only 13.8% of drugs successfully pass clinical trials. It costs $161 million to $2 billion for any drug to complete the entire clinical trials process to get FDA approval. Dr. Charles Wright, PreScouter project architect, says “AI has the potential to revolutionize the current timescale and scope of drug discovery and development.”

Regina Barzilay, a computer scientist at MIT and a scientific advisor for drugmaker Janssen, says “When we study chemistry, we definitely study a lot of rules and we understand the mechanism, but sometimes they’re really, really complex. If a machine is provided with a lot of data, and the problem is formulated correctly, it has a chance to capture patterns which humans may not be able to capture.” AI in the pharma industry is expected to play a role in drug target identification and validation, target based, phenotypic and multitarget drug discoveries, drug repurposing and biomarker identification.


The National University of Singapore (NUS) harnessed AI in the pharma industry to better treat a patient with advanced cancer. AI was used to continuously identify the optimal doses of each drug positively impacting the efficacy and safety of the treatment. “A patient’s clinical profile changes over time. The unique ability for AI to rapidly identify the drug doses that result in the best possible treatment outcomes allows for actionable and perpetually optimized personalized medicine,” said Professor Dean Ho of NUS.

Personalized Medicine

IBM Watson for Oncology is a leader in AI in the pharma industry for personalized treatment decisions in the oncology space. Watson correctly diagnosed a rare form of leukemia in a patient originally thought to have acute myeloid leukemia by examining millions of oncology research papers in 10 minutes. Watson then successfully diagnosed the patient and recommended a personalized treatment plan.

Deep Learning shows great promise for diagnostic purposes as it can be used to accurately analyze pathology, dermatology, ophthalmology and radiology images. Currently, deep learning is about 5-10% more accurate than the average physician. Overcoming the medical culture that values physician intuition over evidence-based solutions can be aided by having medical schools include AI in their curriculum and creating fellowship programs in AI.

Drug Adherence

Traditional methods to measure drug adherence require patients to submit the data themselves without any evidence of them taking a pill or other type of treatment. AiCure, a mobile SaaS platform, has developed an image recognition algorithm that tracks drug adherence by videoing the patient swallowing a pill. The facial recognition system then confirms that the right person took the pill. Cumulative adherence rose from 71.9% to 89.7% for those using AiCure.

Clinical Trials

Advanced predictive analytics can analyze genetic information faster and more comprehensively to identify the appropriate patient population for a trial. IBM Watson enables clinicians to find a list of clinical trials for an eligible patient quicker and easier than conventional methods. Watson can analyze all of the structured and unstructured information from a patient’s medical records in real time and then use deep natural language processing and a reasoning algorithm to look closer at their symptoms and health status matching that to a clinical trial requirements database. Natural Language Processing is an AI technique that can revolutionize treatment in the pharma industry.

Commercial Analytics

Sales reps can use AI in the pharma industry to sift through 40 reports and find the top three insights to give to a doctor today. The digitizing of medical records and broad mobile phone ownership, and the data associated with that, has given pharma the data needed to more quickly and better serve their physicians and patients.


All ten of the big pharma companies, Novartis, Roche, Pfizer, Merck, AZ, GSK, Sanofi, Abbvie, BMS and J&J, have either collaborated with or acquired firms utilizing AI technologies. They are leveraging AI even with limitations due to their IT infrastructure and unfamiliarity and fear of a “black box” technique. Michael Ringel, Managing Partner at Boston Consulting Group, says, “In theory, AI and automation can support, complement or replace any activity that a human does.” The pharma industry should and is taking advantage of the innovations this technique can offer. It is critically necessary for their future solvency.

Interested in discussing the impact of AI on your pharma career? Contact Smith Hanley Associates’ Biostatistics Executive Recruiter, Nihar Parikh at nparikh@smithhanley.com.

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