Artificial Intelligence in Pharmacovigilance Market Size, Share, Growth, Report 2025 to 2034

Artificial Intelligence in Pharmacovigilance Market Size and Growth 2025 to 2034

The global artificial intelligence in pharmacovigilance market size was valued at USD 600 million in 2024 and is expected to hit around USD 1,992 million by 2034, growing at a compound annual growth rate (CAGR) of 21.5% over the forecast period 2025 to 2034. The pharmacovigilance market is increasing with the use of artificial intelligence for better drug safety. Companies quickly analyze huge volumes of data, which allows the detection of side effects and risks faster with AI. The strict rules by the governments and health agencies regarding the safety of the drugs push the companies to employ advanced monitoring systems. Moreover, the increase in new medicines and vaccines necessitates better safety checks. Pharmaceutical companies want to reduce costs and improve efficiency. Therefore, they invest in AI-based solutions. More partnerships between tech companies and drug firms are driving innovation. As patient safety becomes a top priority, AI-powered pharmacovigilance is becoming essential for faster, more accurate monitoring of medicines worldwide.

It is the use of smart computer systems in monitoring and improving drug safety for pharmacovigilance. AI assists in detecting side effects, detecting risks, and processing vast volumes of medical data speedily. This market is growing because drug companies and health agencies need better ways to track medicine safety. Some trends in this market are machine learning, automation, and real-time data analysis. Increasingly, companies are utilizing AI to observe stringent drug safety regulations and decrease expenses. Future growth for AI will come through improved technology, more partnerships, and new government policies. The more medicines available on the market, the more AI systems will be required for rapid and precise monitoring of drugs worldwide.

• In January 2025, the FDA published draft guidance titled "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision Making for Drug and Biological Products." That can be seen as an indication that the FDA is willing and committed to the embedding of AI within drug development. That guarantees the industry the right use of AI-based tools to evaluate drug safety, effectiveness, and quality. The document emphasizes a risk-based regulatory framework that encourages innovation while still protecting the patients involved. The CDER at the FDA is focused on ensuring that the use of AI will be a positive development both in the development process and also in the improvement of patient outcomes.

Artificial Intelligence in Pharmacovigilance Market Growth Factors

AI Technologies Adaptation

• One of the big growth drivers. Pharmacovigilance adopts more and more Artificial Intelligence technologies such as machine learning and natural language processing. As such, using these, the detection of ADRs becomes really quick and smooth. A whole lot of increased data processing by them will further be made more efficient to aid drug safety analysis by size companies in real-time. New requirement for AI-enabled pharmacovigilance with enhanced AI technologies is the growing trend that encourages growth in the market.

Increasing interest in patient safety

• The growing importance of patient safety drives the growth in the pharmacovigilance market. It is because more and more attention is being drawn by regulatory agencies and pharmaceutical companies towards ensuring safety post-market rather than pre-launch to avoid side effects. More and more, the concern grows about the implications of drugs for public health; therefore, it is expanding with the need for efficient pharmacovigilance systems.

Regulatory pressure and compliance requirements

• To be candid, the growing pharmacovigilance market is spurred by stricter regulatory standards and requirements in terms of safety monitoring. Agencies like the FDA and EMA for the former are enforcing more stringent set recommendations regarding the drug's safety and post-market surveillance. Pharmaceutical firms have to spend on better systems to meet this requirement. The imminent threat of recalling the products, financial fines, or a dent in the reputation due to safety concerns compels companies to implement AI-based pharmacovigilance tools, thereby further expanding the market.

Post-Market Surveillance Demand

• Growing demands in the field of post-market surveillance are adding growth to the pharmacovigilance market. After launching the drug, continued monitoring must be done on whether there will be any adverse reactions or a problem with drug safety that did not come forth in the process of clinical research. AI-powered tools are facilitating real-time safety data tracking from pharmaceutical companies that lead to the swift discovery of probable hazards. As more and more people begin demanding post-market surveillance, the growth of AI-powered solutions to complement these efforts is on the rise.

Thriving Pharmaceutical Industry

• The growth of the pharmaceutical industry, with new drugs, is contributing a lot to the growth of the pharmacovigilance market. Pharmaceutical companies are under pressure to ensure drug safety and comply with regulations due to the rising number of new drugs being approved. The demand for strong pharmacovigilance systems is increasing with the growing volume of safety data and continuous monitoring. The increasing need for AI solutions that can effectively manage high volumes of data produced will further drive growth in the market.

Report Scope

Artificial Intelligence in Pharmacovigilance Market Trends

AI with RWD

• The more extensively entwined with RWD for further progress on drug safety surveillance, the AI will move forward further in the identification of ADRs. Through electronic health records, patient registries, insurance claims, and even analysis of the "wisdom" found on social media, AI will better and earlier identify ADRs. Such data will paint an inclusive picture of drugs and their varied impacts on other patient populations outside of clinical trials. All these trends are to provide different signals that can be detected better and, therefore, enable effective and timely regulatory action, patient safety measures, and so on.

AI in Signal Detection and Risk Management

• Big datasets, clinical trials, adverse events, and real-world data can use AI techniques to identify more and more signals with higher precision through advanced detection systems. Leads to the pharmacovigilance system enhancing it with quick potential risk detection and better assessments. Through artificial intelligence-driven signal detection tools, pharmaceutical companies, as well as bodies of regulation, improve their response-ability to emerging issues of safety from drugs, so that better patient safety and sound risk management come into play.

Artificial Intelligence-empowered Pharmacovigilance Collaboration

• Real-time data as well as the insights of shared pharmacovigilance from pharmaceutical companies to healthcare providers along with regulatory bodies are being widely adopted through collaboration platforms empowered with AI. The main ability of these platforms is to process a large amount of information in a way to picks up the signals of potential harm from sources scattered across many aspects of its lifecycle. The improvement of adverse event reporting precision, strengthening compliance, and providing faster response to new drug safety issues with better outcomes for both patients and health systems is brought about by fostering AI.

Use AI for post-marketing surveillance

• It analyzes the large-scale data from different sources to help in the post-market surveillance of drugs. Safety monitoring of drugs after launching increasingly uses tools of AI. AI continuously analyses adverse event reports, clinical data, and even social media in order to catch safety signals and trends in real-time. It is helping companies and regulatory bodies identify problems quickly so they can act quickly, reducing the risks of new drug products. 

AI and Personalized Drug Safety Monitoring

• AI may assist in finding out the risks for specific ADRs based on individualized safety monitoring for personal treatment. This trend will make drugs safer by making them better suited for diverse patient populations, which goes ahead to increase the precision of safety monitoring while reducing the risk of adverse reactions in specific patient groups.

Artificial Intelligence in Pharmacovigilance Market Dynamics

Drivers

Advanced AI technology

• AI is progressing rapidly, allowing companies to monitor medicine safety much more accurately. Machine learning and natural language processing are AI tools that help companies find early effects of harmful drug effects. The tools help check reports faster, reduce mistakes, and follow government rules. With AI continually improving, more companies are using it to enhance drug safety and cut costs. Therefore, AI has become a massive factor in the growth of the pharmacovigilance market.

Stricter Drug Safety Rules

• Drug safety rules have been made more stringent by governments and health organizations like the FDA and EMA. Companies have to check for side effects and report them appropriately. Failure on their part invites penalties or product recalls. AI assists companies in following these rules by making reporting faster and less error-prone. This raises the demand for AI in pharmacovigilance, thus ensuring better patient safety and legal compliance. More companies invest in AI now to meet standards on safety, hence avoiding heavy financial losses.

Restraints

High implementation costs of AI

• AI tools and systems are expensive to establish, especially for small pharmaceutical companies. Money has to be spent on software, data storage, and experienced workers. Many companies cannot afford these expenses, which slows AI adoption. Although AI can lower the cost of pharmacovigilance in the long run, the high cost of investment remains an issue. Companies that have a limited budget might have difficulty switching from manual pharmacovigilance to AI-based systems.

Privacy and Data Security Issues

• This involves a large amount of patient data. The major challenge is to keep these data away from cyberattacks and leaks. Laws such as GDPR and HIPAA require data safety in the company's hands. Fines, loss of trust, and other issues will hound companies.

 Opportunities

Application of Real World Data in Drug Safety

• For AI to be assessing RWD from patient records hospital data and even insurance therefore enhancing drug safety. This detection of harmful effects of drugs by AI occurs before it is widespread and across varied patient groups. Companies can examine large data with AI and better identify safety risks faster. By phasing together, AI and RWD, firms can enhance the safety monitoring of a drug and even make better decision-making in their safety profile.

Growth in Emerging Markets

• Countries in Asia, Latin America, and Africa are enhancing the quality of health care. Regions in these emerging markets require proper monitoring of safer drugs. Monitoring side effects requires efficient AI pharmacovigilance. Greater regulatory compliance pressures in these geographies will attract more companies for AI solutions, thereby opening tremendous opportunities for growing AI companies with a presence across these emerging geographies. Drug safety can, therefore, improve with cost-efficient AI solutions to these regions.

Automation Replaces Manual Labor

• Aspects of drug safety are being done away with to be automated into AI-powered monitoring by pharmaceuticals. AI facilitates the processing of reports, the identification of side effects, and the analysis of safety data at a much faster rate than human beings. The change enhances efficiency and reduces errors. Companies using AI have a competitive advantage since they can track drug safety faster. Automation is now becoming a standard practice in pharmacovigilance, and AI has become an essential tool for drug safety.

New Controls over the Use of AI in Medication Safety

• The rules of pharmacovigilance are now being revised by governments to incorporate AI-based technologies. As the role of AI becomes more inclusive of drug safety, regulatory agencies update their new standards for accuracy and reliability. This will give companies an understanding of how to use AI without violating any regulation. Pharmaceutical companies need to stay abreast of the changing rules and regulations in order not to face fines. The role of AI is revolutionizing pharmacovigilance, and businesses must adjust to the new dynamics.

Challenges

Lack of Standardized AI Regulations

• Rules on the use of AI in pharmacovigilance vary across different countries. It is a challenge for pharmaceutical companies to adhere to all of them. There are legal requirements that report and risk assessments produced by AI have to meet, which slows the pace of adoption around the world. Companies are also uncertain whether their AI systems will get regulatory approval. They have to adapt continuously as rules change but simultaneously need AI to operate within the bounds of legal and ethical boundaries. Until a uniform set of regulations is developed, using AI widely in monitoring drug safety will prove difficult.

Limited AI Experience in Pharmacovigilance

• There are not enough experts who know about both AI and drug safety. Pharmaceutical companies require professionals who can use AI, strictly following the pharmacovigilance rules. However, most companies do not have in-house AI specialists so it is going to be challenging to use AI effectively. Training an existing team requires some time and money. Companies without proper knowledge may face immense difficulties while understanding AI-generated drug safety reports. The industry needs to invest in AI training, collaborate with technology companies, and hire skilled professionals to ensure AI can be used properly in pharmacovigilance.

Artificial Intelligence in Pharmacovigilance Market Segmental Analysis

Application Analysis

Adverse Event Detection and Reporting: AI finds and reports the adverse effects of medicines quickly by thoroughly analyzing immense data extracted from hospitals, social media, and medical records to identify risks early on. It thereby enables doctors and drug companies to take remedial steps faster to ensure a patient's safety. AI makes the reporting of such incidents to health authorities more accurate and reduces human errors. Automation has freed the drug safety teams of paperwork rather than bringing the focus to solving problems. It ensures better patient care and that medicines are apt for the use of individuals.

Signal Detection and Data Mining: AI checks vast amounts of data for its sensors, as it always maintains hidden safety issues about a drug for which a new signal is detected. It is also reading schemes in patient pages, doctor's notes, and even social media for signs or symptoms of their unusual actions. By now, pharmaceutical companies and regulators can easily take precautionary actions concerning any safe event before danger smacks. Machine learning makes the process better with time, which means that the drug safety check ends up being more accurate. AI is very helpful in sorting big data so that identifying a pattern that would be hard for a human expert is easy. Today, early detection of risks will convert into more and better process medicines available.

Case Processing and Automation: AI speeds up the gathering and processing of reports about drug reactions. It checks and organizes data automatically, preventing time and human mistakes. This shortens the time pharmaceutical companies need to address safety concerns. AI verifies that the document complies with health regulations, thereby blocking possible lawsuits. With automated case processing, drug safety teams work more efficiently; most of it is spent on considering innovations for improving medicine safety rather than clerking. This saves a lot of time money, and better drug monitoring at large.

Predictive Analytics: AI can predict medical risks before they become serious problems. It can give warnings to doctors and drug companies before side effects become apparent for actions to be taken avoiding the damage to anybody. Predictive AI will increase safety in medicine by studying trends that would otherwise go unnoticed. Such modifications to medicine guidelines reduce dangerous reactions from companies' and regulators' views. Preventing problems before they occur tends to make drugs safer and, at the same time, protects the public at large.

Technology Analysis

Machine Learning (ML): Machine learning (ML) helps AI exploit its memory for old drug data to discover patterns. It can comb through millions of reports to decide where the bad reactions to medicine were. It is becoming increasingly accurate with time, so drug safety monitoring is seeing possible improvements. Pharmaceutical companies can quickly spot problems with ML, thus shortening the gap in safety -reporting time for adverse effects or even eliminating the gap. On top of this, ML will make better predictions concerning new drugs to warrant their use by the masses. It has improved drug tracking dramatically, making it faster and more efficient in the process.

Natural Language Processing (NLP): NLP helps AI to know the doctor's notes and online reviews of the patients. Such texts become easy for NLP to evaluate as a result of massive volumes into detection of potential unnoticed warning signs for medicines in huge blocks of text. NLP allows drug safety teams to draft side effects from various sources, hence more accurate reporting. This improves the situation for pharmaceutical companies and regulators, as it becomes easier to profile medicines. By taking data and processing it effectively, NLP makes the move toward corrective action possible whenever a safety concern develops.

Robotic Process Automation (RPA): RPA is of great help in carrying out similar drug safety work, that is, automating the data entry, filing of reports, and processing of documents with minimal human error. This will speed up the tracking and reporting of adverse drug events by the companies. With robotic process automation, drug safety teams can devote their time to solving issues rather than admin-related tasks. It not only increases productivity but also enables compliance with regulatory standards. This also entails the removal of redundant work as a cost-saving avenue and improvement of accuracy in tracking medicines' safety.

Deep Learning: Deep learning is a subcategory in AI that understands data complexity. Therefore, this processing deals with the bulk of information to detect problems in the early phases. Deep learning can already learn along with metrics in old cases to anticipate risks of medicines that are not clear. It can modify message warnings and thus help to provide faster and more accurate monitoring of drugs. Companies use deep learning in their mission to make internal decisions and develop smarter drugs. In the future, as deep learning gets better, it will only increase its involvement in preventing reactions between medicines and patients.

End-Use Industry Analysis

Pharmaceutical Companies: Pharmaceutical companies are now embracing AI to make their drugs markedly safe. Early detection of side effects by AI makes medicines more secure. With these automated systems, fewer mistakes are made, and the safety checks are carried out much faster, making compliance a lot easier for AI- as countries become stricter with the rules, AI helps keep them legally compliant and even protects patients according to regulatory guidelines. All this is dependent on how safety regulations will likely be increasingly tighter; thus, AI will help compliance and patient protection while saving time and money for many manual tasks. AI ensures that drug monitoring by pharmaceutical companies is better to ensure that the public is safe.

Contract Research Organizations (CROs): CROs assist drug companies in testing medicine safety. AI speeds things up for drug companies and brings ever-more accurate results. It is useful in the collection of data and analysis of patient safety data, which can enhance the research quality. AI speeds up report writing and checks data for compliance with safety regulations. All this enables CROs to better their services that help drug companies sell safer medicines with the help of AI. Tools driven by AI will lessen the chances of human error and ensure early identification and reporting of drug safety issues.

Regulatory Agencies: These regulatory agencies-that is, the FDA and the EMA- have incorporated AI into their systems to check medicine safety through upload. Because of this, AI makes a huge amount of safety data scanned, thus improving diagnosis on drug safety, as well as identifying potential threats to public health. Besides, governments can combine AI with effective control, analysis, and collection of dissimilar safety data from around the globe. Applications of AI can learn from the massive toll of all existing safety databases. AI will analyze the symptoms that would show safety-concern signs across all the countries just to get a preliminary warning about adverse events that deal with medicines.

Healthcare professionals: Hospitals and clinics use AI to monitor medicine reactions in patients. AI helps doctors identify side effects early, thus preventing serious health problems. It analyzes patient data to find hidden drug risks. This allows healthcare providers to change treatments if needed, improving patient safety. Through AI, better treatment plans are prepared, as it knows how different patients react to medicines. Hospitals provide drugs safely and better care to patients through the application of AI.

Artificial Intelligence in Pharmacovigilance Market Regional Analysis

The AI in pharmacovigilance market is segmented into various regions, including North America, Europe, Asia-Pacific, and LAMEA. Here is a brief overview of each region:

North America: The AI user is mostly dominated by the North American region. The U.S. is the most advanced in technology, and it also has the most stringent regulations to force companies into using AI. Most of the pharmaceutical companies and hospitals are using AI to enhance medicine monitoring. The FDA also encourages AI-driven safety checks, which means faster and safer approval of drugs. As more investments grow in healthcare technology, North America will lead in AI-driven pharmacovigilance.

Europe: Europe is embracing AI in drug safety since health regulations are stringent. The European Medicines Agency (EMA) is encouraging the adoption of AI to enhance the monitoring of medicine. European pharmaceutical companies are exploiting AI to detect side effects faster, upon which they report them fast. AI ensures that strict European safety standards are met while becoming efficient. AI adoption in pharmacovigilance is emerging in Europe as the requirement for safer medicines surges.

Asia Pacific: Asia Pacific is burgeoning in AI-driven drug safety rapidly. China and India are looking to invest heavily in AI as a way to enhance healthcare monitoring. Because pharmaceutical research has been growing steadily in the region, AI allows companies to follow medicine safety far better. It is also compelling governments to be more encouraging with AI adoption toward better regulation of drugs. Awareness of drug safety is increasing. AI plays an increasingly significant role in medicine quality across the Asia Pacific.

LAMEA: Pharmacovigilance AI use is slowly growing in Latin America, the Middle East, and Africa. The drug safety system in these areas is still under development, though enthusiasm for AI is growing. Governments and healthcare systems are now utilizing AI to better monitor medicine safety. In these regions, as infrastructures about healthcare grow, more usage of AI will emerge, and with it, safer medicines for patients.

Artificial Intelligence in Pharmacovigilance Market Top Companies

• Accenture
• Capgemini
• Cognizant
• IBM
• Wipro
• Aris Global
• BioClinica
• ICON
• IQVIA
• ITClinical
• LabCorp
• Linical Accelovance
• Parexel International
• United BioSource
• TAKE Solutions

CEO Statements

Kristof Huysentruyt, Senior Director and Head of Safety Data Management and Systems at UCB Pharma:

• “The patients who benefit from our products are UCB’s number one priority. Accenture INTIENT Pharmacovigilance will help us rapidly process data to identify potential events or issues, while reducing the time and cost needed to deliver a higher level of patient safety. This solution helps us walk the talk of being patient-driven.”

John Balian, Senior Vice President of Global Pharmacovigilance and Epidemiology at Bristol-Myers Squibb:

• “Working with Accenture, Bristol-Myers Squibb will continue to access world-class talent to deliver on our regulatory obligations, while enhancing our focus on patient safety.”

Recent Developments

• In January 2025, Accenture announced the expansion of its INTIENT Pharmacovigilance platform to enhance drug safety monitoring through advanced AI capabilities, aiming to streamline data processing and improve patient safety outcomes.
• In October 2024, the European Medicines Agency (EMA) released new tools and guidelines for the integration of artificial intelligence in pharmacovigilance practices, promoting technology adoption for better drug safety monitoring.
• In August 2024, ArisGlobal launched the USFDA Adverse Event Reporting System (FAERS II), an electronic safety reporting platform powered by LifeSphere MultiVigilance to enhance drug safety reporting capabilities.
• In February 2022, Sanofi and Deloitte collaborated on the ConvergeHEALTH Safety platform, a next-generation AI software-as-a-service solution aimed at transforming pharmacovigilance processes and addressing operational safety issues in the industry.

Market Segmentation

By Technology

• Machine Learning
• Natural Language Processing (NLP)
• Deep Learning

By Application

• Adverse Event Detection
• Signal Detection
• Data Integration & Management
• Drug Safety Monitoring
• Automated Report Generation

By End-User

• Pharmaceutical & Biotechnology Companies
• Contract Research Organizations (CROs)
• Regulatory Agencies
• Healthcare Providers

By Region

• North America
• Europe
• APAC
• LAMEA