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Nov 18th, 2021
AI for Drug Safety Case Processing

Drug Safety Case Processing: What role can AI play?

A major challenge in drug safety is the management of information. AI can help to manage the huge amount of data in an efficient and scalable manner.

The pharmaceutical industry has been exploring and developing AI for drug safety case processing for many years. The main driving factor is the need to process a significant number of case reports that can run into millions or even billions.

AI in Drug Safety Case Processing is the answer to help deal with the problems faced during manual processing of regulatory compliance by automating regulatory submissions, monitoring and improving quality reporting, and reducing reporting timeframes.

Ways AI is Revolutionizing Drug Safety Case Processing

The use of AI in drug safety case processing is becoming more widespread. These AI systems are used to help with many aspects that would otherwise require significant manual labor. They can help identify which drug has the adverse effect, the patient who has the adverse effect, and where it occurred. An AI can also predict potential side effects and prevent them before they happen.

Drug safety case processing is no longer just about identifying how many people were affected by one particular drug or incident; now, it’s about the much larger scope of medical research and development.

Healthcare professionals are facing new challenges due to the increase in the number of people with chronic conditions, record-breaking drug prices, and skyrocketing medical bills. AI can help doctors by taking on some of their most tedious tasks.

The use of AI help companies in the clinical trial process by processing and analysing drug safety case data. This helps in understanding clinical trial data for better research and development.

The use of AI has many benefits for the clinical trials, such as: it provides real-time feedback to the researcher, and helps in reducing human bias in decision making. It can even provide a warning to health care professionals based on certain symptoms or changes in the patient’s condition.

How Using AI for Drug Safety Cases Can Reduce Trial Costs And Improve Clinical Data

Clinical trial costs have been increasing in the past decade. It is difficult to find a treatment for rare diseases because there are not enough patients who are willing to participate in the trial. Moreover, the time and money constraints lead to incomplete or inaccurate data, which can compromise data quality.

Therefore, it is necessary to use AI in clinical trials for drug safety cases by improving trial costs and data quality. AI has helped scientists better understand drug safety cases by linking heterogeneous datasets together, modeling complex biological processes, and predicting adverse events before they happen. The use of AI will decrease the time required for drug development while maintaining high-quality clinical data.

AI can help to expedite drug safety case processing by generating a high volume of data from the raw data input. This allows for a more thorough, accurate and faster detection of adverse events to happen, which will also help to provide better patient care.

Datafoundry helps with AI solution for Drug Safety Case Processing

DF mSafety AI is a modern, cloud-based SaaS that uses the power of AI/ML to deliver efficiencies and a great user experience in Safety Case Management and Signal Detection for medicinal and cosmetic products and medical devices.

Built by industry experts with decades of experience joining hands with a team of world class data scientists and ML pioneers, mSafety is the solution to address your key challenges in Safety and Surveillance Management – from case intake and triage through medical review and regulatory submission.

The below key components of DF mSafety AI are also available as stand-alone products that can be integrated with customers’ existing Safety Databases.

Talk to our experts to know how we can help you in automating Drug safety management.


  1. (accessed October 1, 2021).
  2. (accessed October 1, 2021).

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