Traditional adverse event monitoring relies on manual reporting, often leading to delayed safety signal detection and regulatory inefficiencies. As the EMA and MHRA push for real-time pharmacovigilance, pharmaceutical companies must embrace AI, automation, and big data to stay compliant and proactive.

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Emerging technologies are transforming drug monitoring by enabling faster AE detection, predictive risk analysis, and automated regulatory reporting. This article explores the latest trends, tools, and best practices for modern pharmacovigilance.

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1. Introduction

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The digital revolution has fundamentally reshaped nearly every aspect of healthcare, and adverse event reporting is no exception. Traditional methods, often reliant on passive reporting and limited information sources, are increasingly challenged by the sheer volume of information available today.

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a. How digital transformation is reshaping drug safety

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The shift to digital pharmacovigilance is revolutionizing drug reliability surveillance, making it faster, more precise, and proactive in safeguarding well being. Traditional adverse event reporting relied on manual case processing, often leading to delays in detecting untoward medical occurrences. Today, big data, and automation are transforming pharmacovigilance by enabling real-time monitoring and predictive analytics.

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What key innovations are driving this shift?

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• AI-powered signal detection — machine learning models analyze vast datasets to identify which signals may be considered drug related, allowing for earlier detection and mitigation than traditional methods.
• Real-time findings monitoring — digital platforms, including EHRs, wearables, and patient-reported info, provide continuous insights into drug effects.
• Automation and compliance — automated workflows streamlining EudraVigilance and MHRA Yellow Card reporting, ensuring faster regulatory responses.
• Predictive pharmacovigilance — AI forecasts potential drug risks, allowing proactive measures before widespread issues arise.

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With regulatory agencies prioritizing digital-first risk strategies, pharmaceutical companies are now strongly encouraged to adopt advanced surveillance solutions, making it no longer optional, but essential.

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A study titled "Application of AI and ML in early detection of adverse drug reactions and drug induced toxicity" shows that using artificial intelligence and machine learning approaches has resulted in a paradigm shift in the field of early ADR and toxicity detection. Applying these methods allows for the rapid, thorough, and precise prediction of probable ADRs and toxicity even before the drug's practical synthesis as well as preclinical and clinical trials. This results in more efficient and safer medications with a lesser chance of drug's withdrawal.

Source: AI and ML-based ADRs and toxicity prediction flow diagram

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b. The growing complexity of pharmacovigilance monitoring

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As the pharma landscape evolves, adverse event reporting is becoming increasingly complex. The sheer volume of real-world information, stricter regulatory requirements, and the rise of new drug modalities make traditional pharmacovigilance methods insufficient.

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What are the key factors driving complexity?

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• Evidence volume and variety
  • Exponential data growth
  • The proliferation of digital health technologies, including EHRs, wearable devices where applicable, and mHealth apps, has led to an explosion of health-related information.
  • Social media and online forums also generate vast amounts of unstructured insights that may contain valuable insights that might reveal adverse events considered unexpected, providing early signals that traditional reporting might overlook.
  • Also, some pharmaceutical companies now operate dedicated contact centers, where patients can call or message about side adverse events.
  • Information originates from diverse sources, with varying formats, structures, and levels of quality.
  • Integrating and analyzing the heterogeneous information presents significant challenges.
• Increased drug complexity
  • Novel therapies
  • The development of complex therapies, such as biologics and gene therapies, introduces new challenges in adverse event tracking, particularly when special circumstances (e.g., off-label use or rare disease contexts) come into play.
  • These therapies may have unique reliability profiles and long-term effects that are difficult to predict, requiring close monitoring to protect overall well being.
  • Polypharmacy
  • The increasing prevalence of polypharmacy (the use of multiple medications) raises the risk of drug interactions and adverse outcomes, which can vary widely in severity— from mild events to life threatening or require inpatient hospitalization, making monitoring drug combinations particularly complex.
• Evolving regulatory landscape
  • Stringent regulations
  • Regulatory agencies are increasingly demanding more comprehensive and timely adverse event information, starting from clinical trials through post-marketing surveillance.
  • Compliance with evolving regulations, such as GDPR and HIPAA, adds to the complexity of data management.
  • Global pharmacovigilance
  • Pharma companies must monitor drug safeness across diverse global populations, each with unique genetic and environmental factors. This global aspect adds layers of complexity to insights collection and analysis.
• Technological challenges
  • Data integration and interoperability
  • Integrating insights from disparate systems remains a major challenge. The lack of interoperability hinders the seamless exchange of information.
  • AI and algorithm bias
  • While AI and ML offers great potential, they also introduce the risk of algorithmic bias. Ensuring fairness and transparency of AI-driven adverse event reporting is crucial.
  • Information overload
  • The sheer amount of records available can lead to information overload, making it difficult to identify meaningful signals.

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c. Why traditional methods are no longer sufficient

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Traditional AE monitoring methods — relying on spontaneous reporting, manual case processing, and retrospective analysis — are struggling to keep pace with the growing complexity of drug safety surveillance.

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What are the key limitations of traditional AE procedures?

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• Delayed risk signal detection — manual reporting depends on healthcare professionals and patients voluntarily submitting ADRs, often leading to underreporting and late identification of risks, including those that could result in inpatient hospitalization and other important medical events.
• High workload and innefficiencies — pharmacovigilance teams spend significant time on manual case assessment, making it difficult to scale operations efficiently.
• Fragmented and unstructured insights — AE information is scattered across regulatory databases, medical records, social media, and online forums, making data integration slow and error-prone.
• Regulatory compliance challenges — with EMA and MHRA enforcing stricter real-time monitoring and reporting requirements, traditional workflows struggle to keep up.

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In essence, traditional methods are limited by their reliance on passive reporting, manual processes, and fragmented information. The digital age demands a more proactive, insights-driven, and patient-centered approach to adverse event reporting.

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d. The role of AI, big data, and real-time surveillance

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The future of AE monitoring lies in AI-driven analytics, big data integration, and real-time surveillance. These technologies are transforming pharmacovigilance by enabling fast signal detection, automated case processing, and proactive risk management, and instant contact with relevant teams when an event escalates — far beyond what traditional methods can achieve.

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AI is transforming patient protection efforts by significantly improving the identification of early sgnals, enabling faster intervention and better prevention of adverse outcomes. Machine learning models analyze vast datasets to uncover serious adverse events that require medical attention  faster and more accurately than manual reviews, cross-referencing outcomes listed in regulatory databases or institutional reports for validation.

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AI-powered tools also have the ability to automate case processing, streamlining data collection, categorization, and compliance reporting for both standard and serious adverse events, which reduces human workload and increases operational efficiency. Additionally, natural language processing extracts critical insights from unstructured sources, such as patient forums, social media discussions, and EHRs, ensuring that no potential concerns are overlooked.

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Big data can integrate information from multiple sources, including clinical trials, real-world evidence, wearable devices, and patient-reported outcomes. This vast pool of insights enables a more comprehensive drug medication reliability profile, allowing regulatory agencies and pharmaceutical companies to detect trends before they escalate. AI-driven pattern recognition helps identify potential hazards concerns early and proactively.

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The shift to real-time surveillance is another game-changer for AE monitoring. AI-driven systems now continuously track digital signals across multiple information sources, enabling instant detection of potential drug medication hazards.

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2. The regulatory landscape — adapting to digital monitoring

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As digital technologies reshape AE monitoring, regulatory bodies in the EU and UK are adapting their frameworks to ensure protection, data integrity, and compliance in an era of AI-driven pharmacovigilance.

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Agencies like the European Medicines Agency (EMA) and the Medicines and Healthcare products Regulatory Agency (MHRA) now recognize the role of automation, big data, and real-time surveillance in improving drug safety reporting and analysis.

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a. EMA & MHRA requirements: how regulations are evolving

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In the EU, EMA mandated that all marketing authorization holders (MAHs) comply with Good Pharmacovigilance Practices (GVP), which include mandatory reporting of adverse events from various sources — including digital monitoring tools and AI-driven analytics — is accurately reported. With the growing use of AI and big data, for example, the EMA has emphasized:

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• EudraVigilance reporting — all AE reports, including life threatening or other serious adverse events must be submitted electronically in a standardized format, enabling faster safety signal detection.
• Automated signal detection — the EMA strongly encouraged machine learning and AI for pharmacovigilance but mandates human oversight to validate risk concerns.
• Real-world data integration — companies are urged to leverage patient-generated observations, social media insights, and wearable device reports, provided they meet privacy and scientific validity requirements.
• GDPR compliance — any AI-driven pharmacovigilance tool must ensure information anonymization and protection, aligning with EU privacy laws.

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Following Brexit, the MHRA has developed an independent pharmacovigilance system, adapting its regulations while still aligning with EU best practices. Key updates include:

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• The Yellow Card Scheme — this incorporates automated info collection and AI-driven risk signal detection, aiming for more efficient AE reporting.
• Increased AI oversight — the MHRA supports AI use in drug safety but stresses the importance of explainable AI models, requiring companies to provide clear documentation on AI decision-making processes.
• Faster decision-making and reporting — post-Brexit independence allows the MHRA to accelerate risk-based decisions, requiring companies to be more responsive to concerns in the UK market.

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Pharma companies are responsible to comply with Good Pharmacovigilance Practices and all applicable EMA and MHRA regulations to maintain timely reporting.

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b. The role of EudraVigilance and Yellow Card Reporting

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Effective adverse event reporting in the EU and UK relies on two key regulatory systems: EudraVigilance (EV) and the Yellow Card Scheme. These platforms serve as the primary databases responsible for collecting, analyzing, and responding to drug risk reports, ensuring that pharma companies comply with evolving regulatory requirements.

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EudraVigilance, managed by EMA, is the central system for adverse reaction reporting across the EU. It collects Individual Case Safety Reports (ICSRs) from MAHs, healthcare providers, and patients, enabling real-time signal detection. Key aspects include:

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• Automated  signal detection and analysis — AI-driven tools scan EV insights to identify emerging risk signals, allowing for proactive risk assessment.
• Mandatory digital reporting — since 2017, all serious adverse events and non-serious reports must be submitted electronically, following ISO ICSR standards.
• Cross-border pharmacovigilance compliance — EV enable information-sharing across all EU member states, ensuring consistent regulatory oversight.

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To comply with EMA pharmacovigilance regulations, pharma companies must integrate automated reporting systems that can accurately document, format and submit AE cases under applicable guidelines for electronic submissions.

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In the UK, the Yellow Card Scheme plays a similar role in monitoring adverse drug reactions, medical device reliability, and vaccine side effects, and other important medical events that might require additional attention or follow-up. Key features include:

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• Expanded digital capabilities — the MHRA has introduced AI-powered AE detection, streamlining how adverse events are identified and categorized.
• Public & HCP reporting integration — the cheme encourages both healthcare providers and patients to report ADRs, improving real-world statistics collection.

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3. Tools and technologies powering digital adverse event monitoring

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The rapid digital transformation of AE monitoring is driven by AI, automation, and big data analytics, making pharmacovigilance faster, more accurate, and proactive. Intelligent surveillance systems are enabling pharma companies to detect safety signals in real time, automate regulatory reporting, and enhance compliance with EMA and MHRA requirements.

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a. Advanced signal detection platforms (AI & Machine Learning)

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Integrating AI and ML into signal detection platforms is changing how we monitor and manage adverse events. They have the ability to process vast amounts of information, identify subtle patterns, and provide timely insights. Let's take a closer look at their capabilities:

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• AI-driven early safety signal detectionAI-powered pharmacovigilance platforms process massive datasets to identify potential hazards faster than human analysis. By analyzing EudraVigilance, Yellow Card reports, social media, and EHRs, AI can:
  • Detect an untoward medical occurrence before widespread adverse reactions occur.
  • Identify hidden patterns and rare ADRs that manual methods might miss.
  • Reduce false positives by distinguishing relevant AEs from noise in large datasets.
• Machine learning for pattern recognition and risk stratificationMachine learning models, designed by researchers adept in data science, learn from historical AE info, refining the procedures for signal detection and improving accuracy over time. This enables:
  • Pattern recognition — identify unexpected relationships between drugs, patient demographics, and side effects.
  • Risk prioritization — rank cases based on severity and frequency.
  • Predictive analytics — forecast potential ADRs based on patient information.
• Real-time monitoring and automated complianceAI-driven real-time monitoring ensure continuous surveillance of AE insights across multiple sources. By integrating with EudraVigilance, MHRA Yellow Card, and global AE databases, AI-powered platforms:
  • Automate case triage and reporting to meet compliance requirements.
  • Provide real-time alerts for potential hazards, allowing faster regulatory action.
  • Enable cross-border collaboration, ensuring consistent drug safety monitoring across EU member states.

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b. NLP for analyzing medical literature, reports & social conversations

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NLP enables the analysis of large volumes of unstructured text. This way, pharma companies and regulators can extract meaningful drug safety insights in real-time, improving early adverse events detection, compliance, and decision-making.

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• Extracting safety signals from medical literature & case reportsRegulatory agencies require pharma companies to continuously monitor scientific literature for adverse events. NLP can:
  • Scan thousands of journal articles, clinical trial reports, and safety reviews, identifying new patterns.
  • Recognize drug-event relationships by analyzing how medications are discussed in medical research.
  • Automate literature screening, ensuring compliance with regulations without manual intervention.
• Analysis of regulatory & spontaneous AE reportsAE reports contain vast amounts of unstructured text, requiring human analysts to manually extract crucial information. NLP automates this process by:
  • Structuring case narratives from Individual Case Safety Reports (ICSRs), improving signal detection accuracy.
  • Identifying inconsistencies, missing information, and trends in AE reporting, reducing errors in regulatory submissions.
  • Categorizing potential hazards on drug type, severity, and frequency, streamlining regulatory workflows.
• Monitoring social media & patient forums for early AE signalsPatient-reporting adverse events on social media, online health forums, and drug review platforms provide real-world evidence for drug safety surveillance. NLP-driven pharmacovigilance platforms can:
  • Analyze patient discussions on social media, identifying mentions of drug side effects and adverse events.
  • Differentiate between misinformation, casual mentions, and genuine ADR reports, reducing false positives in signal detection.
  • Translate multi-language content to improve cross-border AE monitoring.
• Sentiment analysis for drug risk trendsNLP-based sentiment analysis detects emerging risk concerns by evaluating:
  • Public perception of medications, based on patient reviews and healthcare discussions.
  • Rising negative sentiment trends, signaling potential issues that require regulatory attention.
  • consumer confidence in specific treatments, helping pharma companies address adherence and risk concerns

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c. EudraVigilance API and data integration: enhancing reporting efficiency

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As pharmacovigilance moves toward automation and real-time adverse event monitoring, integrating with the EudraVigilance API is becoming essential for pharma companies operating in the EU. The EMA mandates that all adverse events reports be submitted digitally, making API-based data integration a crucial tool for ensuring compliance.

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With this API integration, companies can:

• Automatically submit AE cases in real-time, reducing delays.
• Ensure any new findings align with the outcomes listed in existing databases.
• Ensure compliance with ISO Individual Case Safety Report (ICSR) standards, minimizing formatting issues.
• Enable direct validation and acknowledgement from EMA, improving transparency and tracking.

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The EudraVigilance API allows direct flow between internal risk monitoring platforms and regulatory databases, ensuring:

• Faster access to real-time adverse events findings, enhancing early signal detection.
• Integration with AI-driven pharmacovigilance tools for automated case analysis and prioritization.
• Cross-functional findings sharing between regulatory team, healthcare providers, and pharma companies.

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Duplicate and incomplete reports create inefficiencies in adverse events monitoring, particularly for serious adverse events, where medical or surgical intervention might be needed, and accurate and timely evidence is critical. This underscored the need to maintain clear contact logs and document all communication regarding case-follow ups. API-driven data integration with EudraVigilance can:

• Automatically flag and filter duplicate adverse events cases, improving reporting efficiency.
• Standardizes risk case submissions, ensuring consistent insights quality.
• Links EV logs with internal drug safety platforms, enabling holistic risk assessment.

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If we also add AI-driven analytics, pharma companies can:

• Detect adverse event trends faster by analyzing real-time information streams from clinical trials and post-marketing sources.
• Predict emerging risk signals using machine learning models.
• Automate regulatory reporting workflows, ensuring that important information is flagger for immediate review.

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d. Predictive pharmacovigilance — AI models for risk forecasting

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A study titled "A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches" explored the application of a machine learning model to detect potential safety problems for two pharmaceutical products. The model aimed to identify adverse events possibly linked to these drugs and assess its capability to detect such adverse events earlier than traditional surveillance methods.

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The ML model demonstrated acceptable accuracy in detecting risk signals and showed potential for earlier detection compared to traditional methods. The research suggests that integrating ML approaches into pharmacovigilance processes can enhance the efficiency and timeliness of signal detection, complementing existing medical judgment.

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How can AI models enhance risk forecasting in pharmacovigilance?

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• Machine learning for early signal detectionBy identifying patterns in historical adverse events info, ML models can predict which drugs, patient demographics, or treatment conditions are more likely to result in adverse events, improving risk mitigation strategies. ML models are have the ability to analyze vast datasets, including:
  • EudraVigilence and Yellow Card adverse events reports
  • EHRs and real-world evidence
  • Clinical trials records, laboratory results, and post-marketing surveillance reports
• Predicting high-risk patients and drug-drug interactionsAI-powered risk stratification models help with prevention, supporting healthcare providers and regulatory agencies to anticipate high-risk patient groups. This allows for personalized recommendations and early intervention measures. These models assess individual patient risk factors for any untoward medical occurrence by analyzing:
  • Genetic predispositions
  • Pre-existing medical conditions and comorbidities
  • Polypharmacy risks (multiple medications taken simultaneously)
• Real-time adverse event monitoring and risk scoringIncoming adverse event reports are continuously analyzed and assigned scored based on severity, incidence, and population impact.
  • Automated AE prioritization ensures regulators focus on the most life threatening concerns.
  • Dynamic risk forecasting models adapt as new information emerges, improving signal detection accuracy over time.
  • Regulatory reporting automation ensures that high risk cases are immediately flagged for review, streamlining compliance.
• AI for post-marketing surveillance and drug lifecycle managementPredictive AI models help pharma companies monitor drug safety throughout the product lifecycle, from clinical trials to post-market surveillance, identifying early signals that may require medical evaluation or intervention.
  • Early risk prediction in clinical trials improves drug development safety.
  • Long-term safety monitoring ensures newly approved drugs remain safe across diverse patient populations.
  • AI-driven benefit-risk analysis helps determine whether regulatory action is necessary.
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4. Conclusion

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As pharmacovigilance continues to evolve, AI, automation, and real-time surveillance are redefining adverse event reporting. These technologies enable faster determination of which incidents are considered drug-related and warrant further investigation.

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Traditional reporting methods are no longer sufficient to keep up with the increasing volume of real world insights, regulatory demands, and the critical need to rapidly identify serious adverse events, before medical or surgical intervention is needed. I

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Instead, AI-powered analytics, predictive pharmacovigilance, and automated compliance workflows are enabling a faster, more efficient, and proactive approach to drug safety monitoring.

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Organizations should remain adaptable, updating their pharmacovigilance practices according to applicable laws, emerging technologies, and evolving needs.

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