AI-powered big data analytics can also assist in the discovery of drug-event associations for populations, enhancing the identification of potential occurrences and enhancing risk-benefit analyses.

1. The Revolutionary Impact of AI on Drug Safety and Pharmacovigilance
● Drug safety, or pharmacovigilance, is an essential step in the drug development
process for ensuring the health and safety of healthcare consumers.
● It also informs drug manufacturers of any adverse reactions (ADRs) that their
products may cause in a particular patient. It is also referred to as PV or drug safety.
● The PV process begins early in the development of a drug with phased clinical trials
to gather information about its efficacy and safety.
● PV entails "identifying, tracking, evaluating and preventing negative outcomes" from
drug therapies. Over the last few years, it has experienced "huge growth":
● This is due to the enormous number of drugs currently being developed, as well as
the requirement that each manufacturer submit evidence of a drug's efficacy and
safety to the Food and Drug Administration (FDA) of the United States and other
comparable organizations around the world.
PV, which continues throughout the drug's lifecycle, typically consists of two main pillars:
Processing of a single case: The manual collection, examination, and reporting of ADRs are all
included in the processing of a single case for Individual Case Safety Reports. The case processing
process typically consumes and occupies a lot of resources.
This resource might be better used for more critical tasks instead of case processing. PV data
volumes are increasing dramatically, which has led to an increase in case processing costs.
Detecting signals or conducting post-marketing surveillance (PMS): They continuously keep
an eye on ADRs. In addition to using clinical data from sources like electronic health records, data
from medical devices, customer surveys, and social media, signal detection also uses clinical data

2. from sources like medical assessments of adverse drug reactions, medical literature, databases, and
clinical trials.
● PMS is crucial in identifying rare benefits or issues with a drug that would otherwise
remain unnoticed for years because it involves long-term monitoring.
● There are a few differences between pre-marketing trials and post-marketing trials.
Typically, pre-marketing trials last only a few months, while post-marketing studies
involve a much larger population, including smaller subgroups that are not
represented in limited clinical studies.
● PMS can also last for an indefinite period of time, whereas clinical trials typically last
for a few weeks or months.
PMS can manifest in three different ways:
1. spontaneously reporting cases to the FDA; c
2. conducting post-marketing studies like clinical studies; and
3. engaging in active surveillance.
Increased costs for traditional PV (and the business case for AI)
ADRs are on the rise due to several factors, including aging populations, increased public awareness,
and pharmaceutical products. It's easy to understand how the costs of PV have risen sharply for
pharmaceutical companies when this is combined with the increased regulatory requirements over
the past few years. The cost of PV is steadily rising in terms of expenditures and resources.
Developments of AI in PV
➔ All these factors have led to a shift in many PV and PMS strategies' emphases from primarily
reactive to proactive risk management using AI tools. This is because these tools are capable
of locating, gathering, and analyzing vast amounts of data quickly.
➔ Free-form text data is common in the healthcare industry, so trained natural language
processing (NLP) and machine learning (ML) algorithms can recognize, extract, and classify
ADR data from this type of unstructured data.
➔ Automation of manual, repetitive, and standard tasks with case processing is possibly the
most significant potential application of AI in PV, particularly the PMS component of PV. Each
case will be processed, which will also free up valuable resources to work on tasks that are
more difficult and add value.
➔ AI-powered big data analytics can also assist in the discovery of drug-event associations for
populations, enhancing the identification of potential occurrences and enhancing risk-benefit
analyses.
➔ NLP algorithms can analyze large datasets from medical literature, medical records, and
other text data in real-time. In this method, signals pointing to unanticipated advantages or
negative effects are watched for by trained analysts and AI.
➔ The signals offer real-world intelligence compared to data mining from controlled clinical
settings, such as how reports can be generated using natural language generation (NLG)
technology, allowing experts to add additional analysis and polish.

3. ➔ The auto-coding of terms used by consumers and non-medical staff to official medical
ontologies is another application of AI in PV. Another use case is the automatic text
conversion of ADRs.
The Drug Safety and Pharmacovigilance Training Programs at Sollers College provide a curriculum
that is in line with current industry demands, is extremely competent, and prepares professionals in
the pharmaceutical industry for a career in this constantly expanding and highly regulated sector.
Sollers College has provided a proven approach to student success and sustained growth by
combining in-depth knowledge of PV programs with unmatched skill development and services for
student recruitment and retention. Through this strategy, student success and sustainability are
enhanced.
Reference by: https://sollers.edu/pharmacovigilance-and-drug-safety-market-trends-and-forecasts/